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Glama

Politics 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?

Annotations already declare readOnly, openWorld, idempotent, and non-destructive hints. The description adds substantial behavioral context: cascading lookup endpoints, explicit handling of unresolved identifiers, behavior for ambiguous matches (figi_candidates), and graceful degradation when external sources are unavailable. This far exceeds the annotation baseline.

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 and well-structured with sections (SUPPORTED TYPES, LEI/FIGI enrichment) and front-loaded with purpose and examples. Every sentence adds value, though it could be slightly trimmed without losing critical information. The length is 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?

For a two-parameter tool with no output schema, the description is remarkably complete. It explains return elements (identifiers, unresolved list, figi_candidates), handles edge cases (non-equity instruments, ambiguous names, non-US issuers), and gives precise input instructions. Nothing an agent needs to call it correctly 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?

Even though schema coverage is 100%, the description adds considerable extra meaning: for 'value' it gives examples, warns against including trailing security-class words for bonds, and distinguishes between company and drug inputs. For 'type' it explains what each type returns (CIK, ticker, LEI, FIGI vs RxCUI, ingredient, brand). This goes well beyond the minimal schema 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 verb ('resolve') and resource ('entity' names to canonical identifiers) with numerous concrete query examples, and explicitly differentiates from siblings by noting it replaces 2-3 manual lookups and should be used 'FIRST whenever you have a name but need an ID.'

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?

Provides explicit when-to-use guidance ('Use FIRST whenever you have a name but need an ID'), detailed input formatting rules (e.g., for bonds pass exactly the issuer name, not the full noun phrase), supported types, and graceful degradation behavior. It also implies when not to use it (when IDs are already known).

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

A4/5.0
Disambiguation3/5

Most tools have distinct jobs and the descriptions are unusually explicit about routing, but there are overlapping entry points: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all sit in the same query/research space. ask_pipeworx_beta even states it currently matches ask_pipeworx exactly, which makes some boundaries genuinely ambiguous despite strong descriptions.

Naming Consistency3/5

The set is uniformly snake_case and has useful families like polymarket_* and pipeworx_*, plus many clear verb_noun names (list_feeds, read_feed, resolve_entity, validate_claim). However, roughly a third of tools use noun-led or adjective-led names (entity_profile, deep_research, recent_alerts, polymarket_arbitrage), so the pattern is readable but mixed.

Tool Count2/5

34 tools is far beyond what a 'Politics Feeds' server needs, and a large portion of the surface (npm dependency scanning, AI visibility, memory, prediction markets, LLM text generation) is unrelated to the stated purpose. This feels like a kitchen-sink monolith rather than a scoped feed server.

Completeness4/5

Within its actual implied purpose as a broad Pipeworx data-research platform, the lifecycle is well covered: discover, resolve, ask, ground, research, compare, validate, monitor, subscribe, and remember are all present. The gaps are minor—no update-subscription operation and no direct cross-feed search for the feeds named by the server—so agents can work around them.

Resources