{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:UB3TNKDBOTGIP4MTX4ZYSQVPLM","short_pith_number":"pith:UB3TNKDB","schema_version":"1.0","canonical_sha256":"a07736a86174cc87f193bf338942af5b33b472b64dac9bc38a9c8637976a44c9","source":{"kind":"arxiv","id":"2504.15546","version":3},"attestation_state":"computed","paper":{"title":"A Framework for Testing and Adapting REST APIs as LLM Tools","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.SE","authors_text":"Jayachandu Bandlamudi, Kushal Mukherjee, Neelamadhav Gantayat, Prerna Agarwal, Renuka Sindhgatta, Ritwik Chaudhuri, Sambit Ghosh, Sameep Mehta","submitted_at":"2025-04-22T02:52:08Z","abstract_excerpt":"Large Language Models (LLMs) are increasingly used to build autonomous agents that perform complex tasks with external tools, often exposed through APIs in enterprise systems. Direct use of these APIs is difficult due to the complex input schema and verbose responses. Current benchmarks overlook these challenges, leaving a gap in assessing API readiness for agent-driven automation. We present a testing framework that systematically evaluates enterprise APIs when wrapped as Python tools for LLM-based agents. The framework generates data-aware test cases, translates them into natural language in"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2504.15546","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2025-04-22T02:52:08Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"375013953114981880e7e6fee03e6141a613c849e4bc6d69b22b8090ee8aa6fa","abstract_canon_sha256":"9b941148e0cf183908cf8a73a330dad2b8dbbdd50cace1f32588af201e087fa4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:10:57.393870Z","signature_b64":"N+hCP4x6eO77IOl9sC8IjVs8qUdiWpavxk2XUN4iQJZAZVdmFy6leo3IZJcNDI8GLUloCg0j7NVXpydMp94iDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a07736a86174cc87f193bf338942af5b33b472b64dac9bc38a9c8637976a44c9","last_reissued_at":"2026-07-05T12:10:57.391990Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:10:57.391990Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Framework for Testing and Adapting REST APIs as LLM Tools","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.SE","authors_text":"Jayachandu Bandlamudi, Kushal Mukherjee, Neelamadhav Gantayat, Prerna Agarwal, Renuka Sindhgatta, Ritwik Chaudhuri, Sambit Ghosh, Sameep Mehta","submitted_at":"2025-04-22T02:52:08Z","abstract_excerpt":"Large Language Models (LLMs) are increasingly used to build autonomous agents that perform complex tasks with external tools, often exposed through APIs in enterprise systems. Direct use of these APIs is difficult due to the complex input schema and verbose responses. Current benchmarks overlook these challenges, leaving a gap in assessing API readiness for agent-driven automation. We present a testing framework that systematically evaluates enterprise APIs when wrapped as Python tools for LLM-based agents. The framework generates data-aware test cases, translates them into natural language in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.15546","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2504.15546/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2504.15546","created_at":"2026-07-05T12:10:57.392085+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.15546v3","created_at":"2026-07-05T12:10:57.392085+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.15546","created_at":"2026-07-05T12:10:57.392085+00:00"},{"alias_kind":"pith_short_12","alias_value":"UB3TNKDBOTGI","created_at":"2026-07-05T12:10:57.392085+00:00"},{"alias_kind":"pith_short_16","alias_value":"UB3TNKDBOTGIP4MT","created_at":"2026-07-05T12:10:57.392085+00:00"},{"alias_kind":"pith_short_8","alias_value":"UB3TNKDB","created_at":"2026-07-05T12:10:57.392085+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2506.23978","citing_title":"LLM Agents Are the Antidote to Walled Gardens","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2602.11224","citing_title":"Agent-Diff: Benchmarking LLM Agents on Enterprise API Tasks via Code Execution with State-Diff-Based Evaluation","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2605.14312","citing_title":"Making OpenAPI Documentation Agent-Ready: Detecting Documentation and REST Smells with a Multi-Agent LLM System","ref_index":3,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UB3TNKDBOTGIP4MTX4ZYSQVPLM","json":"https://pith.science/pith/UB3TNKDBOTGIP4MTX4ZYSQVPLM.json","graph_json":"https://pith.science/api/pith-number/UB3TNKDBOTGIP4MTX4ZYSQVPLM/graph.json","events_json":"https://pith.science/api/pith-number/UB3TNKDBOTGIP4MTX4ZYSQVPLM/events.json","paper":"https://pith.science/paper/UB3TNKDB"},"agent_actions":{"view_html":"https://pith.science/pith/UB3TNKDBOTGIP4MTX4ZYSQVPLM","download_json":"https://pith.science/pith/UB3TNKDBOTGIP4MTX4ZYSQVPLM.json","view_paper":"https://pith.science/paper/UB3TNKDB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.15546&json=true","fetch_graph":"https://pith.science/api/pith-number/UB3TNKDBOTGIP4MTX4ZYSQVPLM/graph.json","fetch_events":"https://pith.science/api/pith-number/UB3TNKDBOTGIP4MTX4ZYSQVPLM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UB3TNKDBOTGIP4MTX4ZYSQVPLM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UB3TNKDBOTGIP4MTX4ZYSQVPLM/action/storage_attestation","attest_author":"https://pith.science/pith/UB3TNKDBOTGIP4MTX4ZYSQVPLM/action/author_attestation","sign_citation":"https://pith.science/pith/UB3TNKDBOTGIP4MTX4ZYSQVPLM/action/citation_signature","submit_replication":"https://pith.science/pith/UB3TNKDBOTGIP4MTX4ZYSQVPLM/action/replication_record"}},"created_at":"2026-07-05T12:10:57.392085+00:00","updated_at":"2026-07-05T12:10:57.392085+00:00"}