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Glama

Bookbrainz

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

A4.6/5.0
Behavior5/5

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

Annotations already mark it read-only, idempotent, and non-destructive, but the description adds rich behavioral detail: cascading through multiple endpoints, returning unresolved identifiers explicitly, using figi_candidates on ambiguity, and degrading gracefully if GLEIF/OpenFIGI are unavailable. No contradiction with annotations.

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

Conciseness3/5

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

The description is front-loaded with examples and the 'Use FIRST' directive, but the company type section is a dense wall of nested parentheticals that is hard to scan. Every piece of information is useful, yet the structure hurts readability enough to keep this below a 4.

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?

For a tool with only two parameters and no output schema, the description covers input constraints, failure modes, ambiguous-result behavior, fallback degradation, and what fields the output contains. Nothing critical is missing for an agent to invoke it correctly.

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%, so baseline is 3, but the description goes well beyond the schema by explaining the value parameter: pass the entity name only, for bonds pass the issuer exactly as printed, and never include trailing security-class words. It also details the type enum behaviors for company and drug in depth.

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 explicitly states the tool resolves a user-spoken name to canonical/official identifiers, with concrete examples like ticker, CIK, LEI, and RxCUI. It clearly distinguishes itself from siblings by emphasizing that it returns the identifiers other tools require as input.

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 strong guidance: 'Use FIRST whenever you have a name but need an ID' and clarifies supported types and edge cases like non-ticker instruments. However, it does not explicitly name alternative tools or state when not to use this tool, so it lacks the full when/when-not/alternatives structure.

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

A3.8/5.0
Disambiguation2/5

The tool set mixes three distinct domains: BookBrainz entity access (browse, lookup, search), Pipeworx data routing (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, suggest_questions), and Polymarket betting analysis. Multiple tools have overlapping purposes, particularly the ask_pipeworx variants and the various polymarket_* tools, which an agent could easily confuse.

Naming Consistency4/5

All tool names follow a readable snake_case convention. While some are single verbs (browse, lookup, search), others use compound patterns (ask_pipeworx_grounded, polymarket_edge_tracker, pipeworx_trending), and there are a few noun-first names (entity_profile, pipeworx_feedback, recent_changes). The inconsistency is minor and does not hinder readability.

Tool Count2/5

With 34 tools, this is a large surface for a server named Bookbrainz, yet only 3 tools (browse, lookup, search) actually serve that domain. The remaining 31 tools are unrelated Pipeworx/Polymarket functionality, making the tool count inappropriate for the stated server purpose and suggesting a mislabeled or overloaded server.

Completeness2/5

The BookBrainz domain is severely incomplete: it offers read-only access (search, browse, lookup) but no create, update, or delete operations for entities. For the broader Pipeworx/Polymarket functionality, coverage is quite deep, but that is not the server's stated domain. This leaves a fundamental gap for the BookBrainz use case.

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