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Glama

Pubchem

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

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnly/openWorld/idempotent/non-destructive, but the description adds substantial behavioral detail beyond that: multi-source resolution via SEC EDGAR, GLEIF, OpenFIGI, and RxNorm; graceful degradation; explicit `unresolved` reporting; `figi_candidates` when ambiguous; and ISIN-to-LEI mapping. 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.

Conciseness4/5

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

The description is long, but it is front-loaded with trigger phrases and the core purpose, and the subsequent detail about supported types and edge cases is operationally useful. Some wording could be tightened, but the structure and flow are logical.

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?

With no output schema, the description carries the full burden of explaining what the tool does and how it behaves, and it covers nearly everything: supported entity types, input formats, ambiguity handling, unresolved identifiers, source labeling, degradation, and cross-source coverage. It is highly complete for a two-parameter tool.

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 meaning beyond the schema: pass the entity name only, not the full noun phrase; for bonds use the issuer exactly as printed; and it gives concrete examples for both `type` values. This meaningfully reduces parameter misuse risk.

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

Purpose4/5

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

The description states a clear, specific purpose: resolve a user-spoken name to canonical identifiers that other tools require, and it enumerates supported types and example trigger phrases. It is clearly about identifier lookup, but it does not explicitly name or distinguish sibling tools like search_by_name or entity_profile, so it stops one step short of a full 5.

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?

The description gives explicit usage guidance: trigger phrase examples and the rule 'Use FIRST whenever you have a name but need an ID.' It does not name specific alternative tools or state when not to use it, but the trigger conditions are concrete enough for an agent to route correctly.

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

A3.6/5.0
Disambiguation2/5

Many tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer factual questions through similar routing; the polymarket_* tools and entity_profile/compare_entities/recent_changes also cover the same ground. The server is named Pubchem but most tools are unrelated, adding another layer of confusion.

Naming Consistency4/5

All tool names are snake_case and mostly follow verb_noun (search_by_name, get_compound, create_subscription, etc.). Minor deviations exist like entity_profile and recent_alerts being noun-first, and the pipeworx_*/polymarket_* prefixes make the set feel more like multiple products than one coherent API.

Tool Count2/5

35 tools is a large surface, and only 4 (search_by_name, get_compound, get_classification, get_synonyms) actually belong to PubChem. The other 31 tools form a broad Pipeworx/prediction-market toolkit that seems unrelated to the server's stated name and purpose.

Completeness2/5

For a PubChem server the coverage is minimal: basic name->CID resolution, compound properties, classification, and synonyms, but no formula search, bioassay, spectra, or list/search by other identifiers. The Pipeworx tools are extensive for general data querying but require accounts/keys for full use, so anonymous agents hit incomplete workflow dead ends.

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