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Glama

Ipma Pt

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

A4.6/5.0
Behavior4/5

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

Annotations already include readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so safety is covered. The description adds valuable behavioral context: graceful degradation of LEI/FIGI enrichment when upstream sources are unavailable, internal cascading through multiple lookup endpoints, and the explicit statement that unresolved identifiers are returned rather than silently omitted. Only minor gap: no statement about rate limits or result shape beyond the citation URL, but the description meaningfully exceeds what annotations provide.

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 dense, but almost every sentence earns its place: it front-loads example queries and the core directive, then packs behavioral details and parameter semantics. The parenthetical asides (e.g., GLEIF ISIN-to-LEI mapping, pipeworx citation) are valuable but make it a wall of text; a touch more structural segmentation would improve scannability. Still, it is appropriately sized for a complex name-resolution tool with only two parameters.

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, the description thoroughly covers input semantics, supported types, what identifiers come back, degradation behavior, and exactly how to phrase the input value. It even anticipates non-ticker instruments and foreign issuers. The only missing element is a concrete example of the JSON response structure, but the description's level of detail is sufficient for an agent to select and call this tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description goes beyond the schema by explaining what kinds of values map to which identifiers (CIK, LEI, FIGI for company; RxCUI for drug), explaining the ISIN-to-LEI mapping for non-US issuers, and giving a concrete anti-pattern example ("NEW YORK ST DORM AUTH revenue bonds" vs "NEW YORK ST DORM AUTH"). This is genuine added semantics, especially for the `value` parameter.

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 starts with concrete user utterances and a clear verb-resource pair: "resolve a user-spoken NAME to the canonical/official identifiers other tools require as input." It explicitly names sibling-like alternatives (use FIRST when you have a name but need an ID) and distinguishes it from tools like entity_profile and compare_entities. The detailed type breakdown for company and drug makes the purpose unmistakable.

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 explicitly states when to use ("Use FIRST whenever you have a name but need an ID") and gives exclusion behavior ("an identifier that could NOT be resolved is stated explicitly under `unresolved` rather than omitted"). It also clarifies edge cases like non-equity instruments that never have a ticker, which prevents misuse for bond lookups. The sibling list reinforces the contrast: resolve_entity is the name-to-ID gateway, not a profiling or comparison 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

A3.9/5.0
Disambiguation2/5

The ask_pipeworx / ask_pipeworx_beta / ask_pipeworx_grounded / deep_research cluster overlaps heavily on question-routing, and the six polymarket_* tools all operate on prediction-market edges and can be confused. The ipma_*, memory, and subscription tools are distinct, but the overlapping clusters create real misselection risk.

Naming Consistency3/5

Snake_case is used throughout and there are clear prefix groups (ipma_*, ask_pipeworx, polymarket_*), but the rest mix verb-first (compare_entities, discover_tools), noun-first (entity_profile, bet_research), and bare verbs (remember, recall, forget). Readable overall, but no consistent verb_noun convention.

Tool Count2/5

36 tools is heavy, and the server name 'Ipma Pt' implies a narrow Portugal-weather service while 31 of the tools belong to a broad Pipeworx data/prediction-market platform. The scope mismatch makes the count feel bloated rather than curated.

Completeness4/5

The dominant Pipeworx domain is well covered: querying, grounded answers, deep research, entity resolution, comparison, claim validation, subscriptions, memory, and tool discovery are all present with few dead ends. Minor gaps exist (e.g., no general web search tool, thin IPMA historical/warning coverage), but agents can work around them.

Resources