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Glama

Noaa Swpc

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 declare readOnly/openWorld/idempotent/non-destructive, and the description adds substantial behavioral context beyond that: cascading internal lookups, graceful degradation when GLEIF/OpenFIGI are unavailable, ambiguous names returning figi_candidates without asserting a result, explicit unresolved identifiers, and source-labeling of every identifier. This is rich, non-obvious behavior an agent needs to know.

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 appropriately detailed for a complex multi-source resolution tool. It front-loads the core use case with natural-language examples and uses structured supported-types sections. A couple of passages are dense and could be tightened, but most sentences add needed guidance.

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 the two parameters, rich annotations, and no output schema, the description covers what an agent needs to call the tool correctly: supported types, accepted inputs, ambiguity behavior, unresolved-identifier reporting, source attribution, and degradation behavior. No major operational gap remains.

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 adds considerable meaning beyond the schema: accepted input forms (ticker, CIK, ISIN, company name, brand/generic name), the ISIN-to-legal-entity mapping behavior, and the critical warning about passing only the entity name rather than the full query phrase. This materially improves correct parameter construction.

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 clear verb and resource: it resolves user-spoken names to canonical/official identifiers required by other tools. It explicitly enumerates supported entity types (company, drug) and the identifier families produced (CIK, LEI, FIGI, RxCUI), which distinguishes it from siblings like entity_profile or 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?

The description gives explicit when-to-use guidance: "Use FIRST whenever you have a name but need an ID," supported by concrete user-phrase examples. It also includes detailed input-format guidance, such as passing the issuer name only and not trailing security-class words. However, it does not name explicit alternative tools or state when not to use this tool.

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

B3.3/5.0
Disambiguation2/5

Several tool clusters heavily overlap: multiple ask_pipeworx variants, five polymarket_* tools, and company-research tools (entity_profile, compare_entities, recent_changes) all have similar purposes. An agent would frequently need to read long descriptions to distinguish between them, and some boundaries remain unclear.

Naming Consistency2/5

Naming mixes verb-first styles (ask_pipeworx, list_subscriptions, subscribe) with noun-only names (kp_index, solar_wind, alerts), and disjointed prefixed families (polymarket_*, pipeworx_*). There is no uniform verb_noun or other consistent convention across the set.

Tool Count2/5

38 tools is well above the typical well-scoped range, especially for a server ostensibly dedicated to NOAA space weather. The count feels bloated, with many tools unrelated to the server's stated purpose.

Completeness2/5

The server name implies space-weather coverage, and that domain has only a handful of tools (alerts, kp_index, solar_wind, etc.), leaving gaps (no proton flux, no Dst index). Meanwhile, the extensive non-space-weather tools are over-provisioned and their inclusion makes the overall surface incoherent and impossible to navigate as a complete domain.

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