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Glama

Pvgis

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
Behavior5/5

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

Even though the annotations already declare read-only/idempotent behavior, the description adds substantial context: it names every identifier returned and its source, states that unresolved identifiers appear under 'unresolved' rather than being omitted, explains ambiguity handling via figi_candidates, and documents graceful degradation when GLEIF/OpenFIGI are unavailable. No contradiction with the 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 long, but nearly every sentence earns its place given the tool's two entity types and multiple identifier sources. It is front-loaded with example queries and a one-line usage rule before diving into details, though it could be tightened without losing meaning.

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?

With no output schema, the description carries the burden of explaining return semantics, and it does: it names all returned identifier fields, documents unresolved and ambiguous cases, and covers failure behavior and cross-source enrichment. For a tool with two types and many edge cases, nothing critical is missing.

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 coverage is 100%, so the baseline is 3, but the description still adds value by explaining accepted input forms (ticker, CIK, ISIN, or company name), giving an ISIN example, and noting the entity-name-only convention for bonds. The value parameter's schema description also covers examples, making the combined guidance above baseline.

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 opens with concrete user phrasings ('What's the ticker for…' / 'what's the CIK for…') and then states a precise job: resolve a user-spoken NAME to canonical/official identifiers other tools require as input. It distinguishes itself from siblings by identifying when it should be used first, and the supported types make the scope unmistakable.

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?

'Use FIRST whenever you have a name but need an ID' is an explicit trigger condition, and the description details what input forms are accepted per type. However, it does not name sibling alternatives (e.g., entity_profile) or state explicit when-not-to-use conditions, so it stops short of a full 5.

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

B3.4/5.0
Disambiguation3/5

Multiple tools serve overlapping query purposes (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) with subtle differences that are hard to distinguish without careful reading. Similarly, prediction market tools (bet_research, polymarket_arbitrage, polymarket_edges) have overlapping scopes.

Naming Consistency2/5

Names are highly inconsistent: verb_noun (ask_pipeworx, resolve_entity), noun_descriptive (entity_profile, polymarket_arbitrage), and simple nouns (tmy, pvgis). No clear pattern emerges, making it hard to anticipate tool names.

Tool Count3/5

34 tools is borderline high for a single server. While some utility tools (remember, forget) are justified, many tools are very narrowly scoped (tmy, generate_llms_txt) and could be merged or omitted.

Completeness3/5

The server covers a wide breadth (data querying, prediction markets, solar energy, AI visibility) but feels like a collection of unrelated domains. Core operations for data querying are present, but the solar tools (monthly_radiation, pv_performance) seem orphaned from the rest.

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