Skip to main content
Glama

Meteors

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. Changed3 schema fields changed
    • changedInput schema / properties / type / description
      Previous value: -"Entity type. v1 supports \"company\"."New value: +"Entity type: \"company\" or \"drug\"."
    • changedInput schema / properties / type / enum
      Previous value: -[
-  "company"
-]New value: +[
+  "company",
+  "drug"
+]
    • changedInput schema / properties / value / description
      Previous value: -"Ticker, CIK, or company name (e.g., \"AAPL\", \"0000320193\", \"Apple\")."New value: +"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\")."
  3. Added

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already mark the tool as read-only, open-world, idempotent, and non-destructive. The description adds meaningful behavioral context: internal cascading through multiple lookup endpoints, graceful degradation when GLEIF or OpenFIGI is unavailable, ambiguous names returning figi_candidates instead of asserting, and unresolved identifiers explicitly listed. There is 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 long and dense, but it is front-loaded with user-facing examples and a 'Use FIRST' directive. The main supported-types section is heavy with nested parentheticals and could be parsed more easily, but almost every clause adds distinguishing information, so it earns its length for a complex resolver.

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 full burden of explaining what the tool returns. It covers returned identifiers, candidate ambiguity, unresolved values, enrichment failure behavior, and the internal cascade. There are no critical gaps for an agent to call this 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?

Schema coverage is 100%, but the description goes far beyond it. It explains the accepted input forms for the value parameter (ticker, CIK, ISIN, name, brand/generic), provides the 'ENTITY NAME ONLY' rule, shows the issuer-name example, and warns that trailing security-class words will break FIGI matching. This is exactly the kind of parameter guidance an agent needs.

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 natural-language queries and states a clear verb-resource relationship: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It names two supported entity types and the specific identifiers produced (CIK, LEI, FIGI, RxCUI), which distinguishes it from siblings like entity_profile or validate_claim.

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 an explicit trigger: 'Use FIRST whenever you have a name but need an ID.' It also details accepted inputs for company and drug lookups. However, it does not explicitly name when-not-to-use it or name sibling tools as alternatives, so it stops just short of the highest guidance standard.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Meteors toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.5/5.0
Disambiguation2/5

Several tools serve nearly identical purposes: ask_pipeworx and ask_pipeworx_beta are explicitly functionally identical right now, and ask_pipeworx, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all overlap as query/discovery entry points. The six prediction-market tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) also blur together for agents looking to find or size a trade.

Naming Consistency2/5

Naming is inconsistent: some tools use get_/list_/scan_ prefixes while others are bare nouns (polymarket_edges, entity_profile, recent_alerts), and the Pipeworx prefix appears only on some tools (pipeworx_feedback, pipeworx_trending) while equivalent tools are named ask_pipeworx or deep_research. Verb styles vary between imperative (validate_claim, resolve_entity) and descriptive (bet_research, polymarket_arbitrage).

Tool Count2/5

34 tools is heavy for a server whose name (Meteors) covers only 3 of them. The bulk belongs to unrelated domains — Pipeworx data access, prediction markets, memory, npm scanning, llms.txt generation — making the surface feel like an unfocused grab bag rather than a deliberate product. Many of the 34 tools could be consolidated (e.g., the three near-identical ask_pipeworx variants).

Completeness2/5

The server has no coherent domain to assess completeness against: meteor data is limited to three lookups with no management/CRUD, memory tools have save/recall/delete but no update, and the Pipeworx surface lacks obvious editing or administrative operations beyond subscriptions. The prediction-market toolset is thorough, but it sits awkwardly beside unrelated utilities, leaving the overall tool surface feeling incomplete for any single stated purpose.

Resources