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Glama

Ai Feeds

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

A4.9/5.0
Behavior5/5

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

The annotations already declare readOnly/openWorld/idempotent/non-destructive, so the bar is lower, but the description adds substantial behavioral context: internal cascading through multiple lookup endpoints, graceful degradation of LEI/FIGI enrichment, explicit listing of unresolved identifiers, source labeling of every identifier, and the exact behavior when a name matches multiple instruments. This goes far beyond the structured annotations and gives the agent a realistic model of the tool's behavior.

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 densely packed with genuinely useful information; almost every clause earns its place. It front-loads the core purpose and the 'use FIRST' directive before moving into detailed type-specific semantics. Some of the parameter detail is also present in the schema, so a slightly tighter version could avoid redundancy, but the extra examples and failure-mode notes justify most of the length.

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?

Given there is no output schema, the description carries the full burden of explaining return values, and it does: it lists canonical identifiers, source labels, figi_candidates for ambiguous results, and an explicit unresolved field. It covers both supported types, acceptable input forms, edge cases like non-US issuers and non-equity instruments, and graceful degradation. The agent has everything needed to invoke the tool correctly and interpret its result.

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?

While schema coverage is 100%, the description adds crucial parameter nuance that the schema alone does not convey: pass the entity name only, never the full noun phrase, with a concrete bond issuer example and the reason ('trailing security-class words match nothing'). It also explains accepted input forms for each type, including the meaning of an ISIN input. This materially improves the agent's chance of supplying a correct value.

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 clearly states a specific verb and resource: resolving a user-spoken name to canonical/official identifiers required by other tools. It explicitly enumerates the supported entity types and input forms, and it distinguishes itself from related tools by emphasizing it is the first stop when you have a name but need an ID. The examples (ticker, CIK, LEI, RxCUI) make the purpose immediately concrete.

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

Usage Guidelines5/5

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

The description gives direct, explicit usage guidance: 'Use FIRST whenever you have a name but need an ID.' It also clarifies what to do in ambiguous cases (returned figi_candidates rather than asserting a match), when a non-equity instrument should be resolved, and that EDGAR identifiers still return if LEI/FIGI enrichment fails. This level of when-to-use detail exceeds a simple cue and tells the agent when the tool is the right choice.

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

A4.1/5.0
Disambiguation4/5

Most tools have distinct purposes, but some overlap exists between query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) and company research tools (entity_profile, compare_entities, recent_changes). Descriptions clarify differences, but an agent might occasionally select the wrong one.

Naming Consistency4/5

The naming is mostly snake_case with descriptive verb_noun patterns (list_feeds, read_feed, remember, recall). However, some tools break the pattern (ai_visibility_check, ask_pipeworx, discover_tools) and there is inconsistency in verb prefixes (ask_ vs. query vs. nothing). Overall still readable.

Tool Count2/5

With 33 tools covering AI visibility, data queries, betting, feeds, memory, subscriptions, and more, the server feels overloaded and lacks a focused scope. Many tools belong to distinct domains, suggesting this should be split into multiple specialized servers.

Completeness4/5

Within each subdomain (Pipeworx data, Polymarket, feeds, memory), the tool surface is quite complete, offering CRUD-like coverage and advanced features. Only minor gaps exist (e.g., no tool for updating a subscription, no direct PolyMarket trade execution).

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