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Glama

Open Notify

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?

Annotations already establish read-only, idempotent, non-destructive behavior, and the description adds substantial behavioral detail beyond that: internal cascading lookups, graceful degradation when GLEIF/OpenFIGI are unavailable, explicit `unresolved` reporting, ambiguous matches returning `figi_candidates`, and identifier source labeling. 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 and densely packed, but nearly every sentence carries operational meaning—examples, supported types, edge-case behavior, and input constraints. It is front-loaded with user phrasing and the core purpose, though the extended parentheticals make it somewhat harder to scan quickly.

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 no output schema and high operational complexity, the description covers the full picture: return fields, unresolved identifiers, ambiguity handling, input variants, source coverage, failure degradation, and cross-type behavior. Nothing essential for correct selection or invocation is missing.

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%, but the description adds significant semantic value beyond the schema: accepted identifier formats (ticker, CIK, ISIN, name), brand vs generic drug names, the issuer-name-only instruction for bonds, and the warning about trailing security-class words. This is exactly the kind of parameter nuance that prevents incorrect invocation.

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 opens with concrete user utterances and then states the exact purpose: resolving a user-spoken name to canonical/official identifiers that other tools require. The supported types are enumerated, and the tool is clearly positioned as the ID-lookup entry point, which distinguishes it from sibling tools like entity_profile and compare_entities.

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?

Explicitly instructs the agent to use this tool 'FIRST whenever you have a name but need an ID', and it clarifies that it replaces 2-3 manual lookups. It does not explicitly name alternatives or say when NOT to use it, but the priority guidance plus the clear supported-type rules give strong usage context.

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

The three ask_pipeworx variants are near-identical (the beta is currently an exact copy of the stable router), and ask_pipeworx, ask_pipeworx_grounded, deep_research, and validate_claim all route natural-language questions to the same underlying source catalog. The six polymarket_* tools also heavily overlap in opportunity detection, though some clusters like memory and subscriptions are clearly separated.

Naming Consistency3/5

The naming is mostly snake_case but mixes conventions: verb_noun (list_subscriptions, resolve_entity), noun-first (entity_profile, bet_research), metadata-style prefixes (pipeworx_trending, polymarket_edges), and bare verbs (remember, recall, forget). It is readable but does not follow one predictable pattern across the set.

Tool Count2/5

33 tools is excessive for a server nominally named Open Notify, and the count is inflated by redundant ask_pipeworx variants and six closely-related Polymarket tools. The broad data-gateway scope could justify a large catalog, but the set feels bloated and unfocused rather than deliberately scaled.

Completeness2/5

For the apparent Open Notify domain, only astros and iss_now fit, and core ISS functionality like pass predictions is missing. The wider data-lookup surface is extensive, but the inclusion of unrelated memory, subscription, npm-scanning, and llms.txt tools means no single domain gets coherent lifecycle coverage.

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