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Glama

Us Iso Grid

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

A4.8/5.0
Behavior5/5

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

The description goes far beyond annotations, detailing internal cascading through multiple endpoints, graceful degradation when GLEIF/OpenFIGI are unavailable, ambiguity handling via `figi_candidates`, explicit `unresolved` fields, and source labeling. It discloses limitations (non-equity instruments, ISIN-to-LEI mapping) and asserts nothing for ambiguous matches. This is comprehensive behavioral disclosure.

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 every sentence contributes: examples, type details, edge cases, and behavioral notes. It is well-structured with clear sections (supported types, behavior, input guidance). Nothing is redundant, though some trimming could tighten it. The front-loading of examples is effective.

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 no output schema, the description explains return format: identifiers with source labels, unresolved lists, `figi_candidates` for ambiguous matches, and degradation behavior. It covers error handling, input pitfalls, and scope. This is fully complete for a complex tool with no output schema.

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 parameters are already described. The description adds significant extra value: for `value` it explains the correct input format, provides examples, and warns against common mistakes (e.g., trailing security-class words). For `type` it explains exactly what each enum yields. This goes beyond schema basics, though not exhaustive.

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 clearly states the tool's purpose: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It provides concrete example queries (ticker, CIK, LEI) and explicitly defines supported types (company, drug). It differentiates from siblings by emphasizing that it is the 'FIRST' step when a name needs an ID, which is unique among the listed tools.

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?

Explicitly says 'Use FIRST whenever you have a name but need an ID.' It gives detailed input constraints: 'Pass the ENTITY NAME ONLY' and warns against including trailing security-class words, with a concrete example. It also explains fallback behavior and that it replaces 2-3 manual lookups, guiding the agent on when this is the right 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

A3.6/5.0
Disambiguation3/5

There are multiple overlapping query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research, discover_tools) that could confuse an agent about which to use. Similarly, several Polymarket tools have overlapping functions. However, descriptions are detailed enough to distinguish most, and energy grid tools are clearly separated by ISO.

Naming Consistency3/5

Tool names consistently use snake_case, but there is no uniform verb_noun pattern. Some start with verbs (ask, compare, scan), others with nouns (entity_profile, recent_alerts), and many use domain prefixes (caiso_, polymarket_). This mixed style reduces predictability.

Tool Count2/5

With 40 tools spanning unrelated domains (AI visibility, energy grids, prediction markets, memory, SEO, subscriptions), the server feels overloaded. A typical focused server would have 3-15 tools; this range indicates scope creep and lack of coherence.

Completeness2/5

The server covers multiple domains but lacks depth in each. For example, energy grid tools cover only three ISOs (CAISO, ERCOT, NYISO), missing many others. The prediction market tools are extensive but incomplete without real-time price updates. The general query tool 'ask_pipeworx' tries to cover everything, but the overall surface is uneven and has notable gaps.

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