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Glama

Irail

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?

Beyond the read-only/idempotent annotations, the description discloses ambiguous matches returning figi_candidates, unresolved identifiers surfaced under an unresolved field, graceful degradation when GLEIF or OpenFIGI is unavailable, source-labelled identifiers, and internal cascading lookups. This materially changes how an agent should interpret results.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but organized: it front-loads natural-language triggers, states a priority rule, then uses SUPPORTED TYPES to separate company from drug behavior. The parentheticals are informative rather than filler, so the length is justified by the tool's actual complexity.

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 still tells the agent what will be returned for each type, what happens under ambiguity or upstream failure, and how ownership data appears. It covers both supported types, input-normalization rules, and edge cases such as non-equity instruments, leaving little for an agent to guess.

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 the schema already documents both parameters at 100% coverage, the description adds high-value guidance, especially the warning to pass only the entity name and never the full noun phrase like 'revenue bonds'. It also gives accepted forms for value (ticker, CIK, ISIN, name; brand or generic drug name) and explains type-specific behavior.

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 names a precise operation: resolving a spoken name to canonical official identifiers that other tools require as input, and it lists concrete outputs for company and drug lookups. The directive 'Use FIRST whenever you have a name but need an ID' separates this tool from ID-consuming siblings.

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?

It gives an explicit trigger ('Use FIRST whenever you have a name but need an ID') and says the tool replaces 2-3 manual lookups, which tells an agent when to reach for it. It does not name specific sibling tools or describe when to use them instead, so it is below a full when/when-not statement.

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

A4/5.0
Disambiguation4/5

Most tools have distinct purposes with detailed descriptions. However, the three ask_pipeworx variants (standard, beta, grounded) and the five polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) have overlapping functionality that could confuse an agent if descriptions are not carefully parsed. The memory tools (remember, recall, forget) are clear. Overall, the set is mostly disambiguated.

Naming Consistency3/5

Tool names use snake_case consistently, but naming patterns vary: some follow verb_noun (e.g., ai_visibility_check, compare_entities), others are noun_noun (e.g., bet_research, entity_profile), and a few are single verbs (e.g., recall, remember, forget, subscribe). This mixed pattern reduces predictability, though the names are still readable.

Tool Count2/5

At 35 tools, the server is overly broad, covering Belgian rail, Pipeworx data queries, prediction markets, memory, subscriptions, and more. The server name 'Irail' suggests a focused rail toolset, but rail is only a small part. This scope mismatch and high tool count make it feel bloated and less coherent.

Completeness4/5

Within each subdomain, the tool surface is fairly complete. Belgian rail has journey planning, liveboard, train tracking, and disturbances. Data querying has universal, grounded, deep research, entity profiles, comparisons, recent changes, and claim validation. Prediction markets have arbitrage, edge detection, edge tracking, fill risk, and cross-venue spread. Memory and subscriptions are covered. Minor gaps exist (e.g., no tool for deleting entities or canceling orders), but overall coverage is strong.

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