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Glama

Openfema

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 provide readOnly/idempotent/non-destructive hints, but the description adds substantial non-obvious behavior: unresolved identifiers are returned under `unresolved` rather than omitted, ambiguous name matches return `figi_candidates`, LEI/FIGI enrichment degrades gracefully, and internal cascading replaces multiple lookups. 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 dense and heavily parenthetical, which hurts scanability, but every major clause adds useful operational information. It is front-loaded with example utterances and the 'Use FIRST' rule, making the primary purpose immediately clear before the deep detail.

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 two complex entity types, the description covers input constraints, output components (CIK, ticker, LEI, FIGI, RxCUI, source labels), special cases (non-equity instruments, non-US issuers, ambiguous matches), and failure behavior (graceful degradation). It is sufficiently complete for an agent to invoke the tool 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?

Although schema coverage is 100%, the description goes far beyond the schema. It details accepted formats for company (ticker, CIK, ISIN, name), drug brand/generic names, and gives a critical usage warning: pass the entity name only, not the full noun phrase (e.g., 'NEW YORK ST DORM AUTH' not 'revenue bonds'). This materially improves correct 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 uses a specific verb ('resolve') and resource ('user-spoken NAME to canonical/official identifiers'), reinforced by concrete example queries. It clearly distinguishes itself from sibling tools by stating it supplies the IDs other tools require as input and positions it as the first tool to use when a name needs an identifier.

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?

Gives explicit guidance: 'Use FIRST whenever you have a name but need an ID', plus clear context about accepted input formats and ambiguity behavior. It does not explicitly name alternative sibling tools or state when not to use the tool, but the use condition is strong and actionable.

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 tool set has several overlapping families: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, the discovery tools (list_datasets, discover_tools, suggest_questions) all serve a 'what can I do here' purpose, and ai_visibility_check is wrapped by scan_competitor_ai_presence. The polymarket_* tools are well-differentiated, but the heavy overlap in the meta-tools makes selection error-prone.

Naming Consistency2/5

Naming is a mix of conventions with no unifying pattern: family prefixes appear as ask_pipeworx_*, pipeworx_*, and polymarket_*, while unrelated tools use bare nouns (entity_profile, recent_changes), verb-first names (validate_claim, search_within), and inconsistent styles. The three actual FEMA tools (disaster_declarations, list_datasets, query_dataset) share no prefix that ties them to the server's stated name.

Tool Count2/5

34 tools exceeds the 'too many' threshold, and the count is unjustified by the server's apparent scope: only 3 of 34 tools relate to OpenFEMA data, with the remaining 31 being a grab-bag of Pipeworx routing, Polymarket betting, memory, subscription, and AI-visibility utilities. The bulk is either redundant with the meta-routers or off-domain for a server named 'Openfema'.

Completeness2/5

For FEMA specifically, list_datasets + query_dataset covers generic read-only access and disaster_declarations adds a convenience wrapper, but the domain is extremely thin and lacks FEMA-specific conveniences (e.g., geographic aggregation, multi-dataset joins, incident summaries). For the broader Pipeworx universe the routing coverage is actually decent, but that makes the FEMA-named server's surface feel incoherent — an agent expecting a FEMA toolset finds most of its value in unrelated prediction-market and brand-visibility tools.

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