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Glama

Owasp

by io.github.pipeworx-io

Resolve Entity

resolve_entity
Read-onlyIdempotent

"What's the ticker for…" / "find the CIK for…" / "what's the LEI for…" / "what's the RxCUI for…" / "look up the ID for…" / "what is X's official identifier" / "who owns X" / "is X a subsidiary of Y" — resolve a user-spoken NAME to the canonical/official identifiers other tools require as input. Use FIRST whenever you have a name but need an ID. SUPPORTED TYPES: "company" (cross-source identity spine: 10-digit CIK + ticker + company_name from SEC EDGAR, legal-entity LEI from GLEIF with parent/ultimate-parent/children ownership when the LEI resolves, and security FIGI from OpenFIGI — by exact ticker map when a ticker is implied, and otherwise by name search, so NON-EQUITY instruments that never have a ticker (municipal and corporate bonds, notes, authority debt) DO resolve here; when a name matches more than one instrument it asserts nothing and returns figi_candidates to pick from, which is the correct answer to an issuer name that does not identify a single bond; every identifier is labelled with the source that established it, and an identifier that could NOT be resolved is stated explicitly under unresolved rather than omitted — accepts ticker, CIK, ISIN, or company name as input; an ISIN like "CH0038863350" resolves to the LEGAL ENTITY that issued the security via the GLEIF ISIN-to-LEI mapping, covering non-US issuers EDGAR cannot reach), "drug" (returns RxCUI + ingredient + brand from RxNorm + pipeworx://rxnorm/concept/{rxcui} citation; accepts brand or generic name). LEI/FIGI enrichment degrades gracefully — if GLEIF or OpenFIGI is unavailable, the EDGAR identifiers still return. Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type: "company" or "drug".
valueYesFor company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., "ozempic", "metformin"). Pass the ENTITY NAME ONLY — for a bond that is the ISSUER exactly as printed ("NEW YORK ST DORM AUTH"), never the question's full noun phrase ("NEW YORK ST DORM AUTH revenue bonds"): the FIGI lookup matches instrument names, so trailing security-class words match nothing.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / properties / value / description
      Previous value: -"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\")."New value: +"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\"). Pass the ENTITY NAME ONLY — for a bond that is the ISSUER exactly as printed (\"NEW YORK ST DORM AUTH\"), never the question's full noun phrase (\"NEW YORK ST DORM AUTH revenue bonds\"): the FIGI lookup matches instrument names, so trailing security-class words match nothing."
  2. First observed

TDQS

A4.7/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnly/openWorld/idempotent annotations, the description discloses important behaviors: ambiguity is handled by returning figi_candidates without asserting a match, unresolved identifiers are explicitly listed under 'unresolved', and LEI/FIGI enrichment degrades gracefully when upstream services are unavailable. It also reveals internal cascading lookups, setting accurate expectations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long and dense, but the key directive is front-loaded and nearly every sentence carries useful operational or behavioral detail. Some examples and parentheticals could be tightened, but the length is largely justified by the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Even without an output schema, the description explains what is returned for each type: CIK, ticker, company name, LEI, parent/ultimate-parent/children ownership, FIGI, RxCUI, ingredient, brand, and citation. It also covers ambiguity, unresolved identifiers, and failure degradation, giving the agent a complete mental model for calling and interpreting results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, yet the description adds critical nuance: for company values it distinguishes ticker, CIK, and name; for bonds it warns to pass the issuer name only and gives a concrete counterexample. This materially improves the chance of correct invocation beyond the schema's basic descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action: resolving a user-spoken name to canonical/official identifiers other tools require. It enumerates supported types ('company', 'drug') and includes concrete query examples, making it clear this is the name-to-ID lookup tool distinct from sibling tools like entity_profile or search_within.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives an explicit when-to-use directive: 'Use FIRST whenever you have a name but need an ID.' It also provides illustrative user expressions like 'What's the ticker for…' and 'who owns X.' However, it does not explicitly mention alternatives or exclusions, so it stops short of full when-not guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.9/5.0
Disambiguation3/5

Many tools overlap in purpose, especially the Pipeworx data query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) and the Polymarket betting tools (bet_research, polymarket_arbitrage, etc.). Descriptions help differentiate, but an agent may still struggle to choose the correct one.

Naming Consistency4/5

Most tool names use snake_case and follow a verb_noun pattern (e.g., ask_pipeworx, resolve_entity, validate_claim). Some deviations exist (e.g., entity_profile, cheat_sheet) but overall the pattern is predictable.

Tool Count2/5

34 tools is excessive for a server named 'Owasp', as only 4 are directly OWASP-related (asvs_chapters, asvs_requirements, cheat_sheet, top10). The remaining 30 tools are for general data querying and betting, making the surface feel bloated and unfocused.

Completeness3/5

The OWASP domain is partially covered with ASVS requirements, cheat sheets, and Top 10 lists. However, notable gaps exist, such as the OWASP Testing Guide, Software Assurance Maturity Model (SAMM), or risk assessment tools. The Pipeworx tools are comprehensive but not relevant to OWASP.

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