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Glama

Chile Procurement

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

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the safety profile is known. The description adds substantial context beyond these: it explains the cascading internal lookups, graceful degradation when GLEIF/OpenFIGI are unavailable, how ambiguous matches return figi_candidates, how unresolved identifiers are explicitly listed, and that non-equity instruments resolve. All of this is useful behavioral detail that complements the annotations without contradicting them.

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 lengthy but well-structured: it opens with query examples, then covers supported types, behavior nuances, and input guidance. Information is front-loaded with the most critical examples at the start. While verbose, every sentence adds operational detail or usage nuance, and the structure aids readability. It is not overly terse, but the complexity justifies the length.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

This is a complex tool with multiple entity types, multiple identifier sources, and edge cases. The description covers many outcomes: dual-type support, ambiguity handling via figi_candidates, graceful degradation, and explicit unresolved fields. However, because there is no output schema, it does not fully specify the response structure (e.g., exact JSON keys). It gives hints but not a complete contract. For an agent to use it correctly, the input guidance is thorough, but the output shape is only partially described.

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% with descriptions for both parameters. The description adds significant value to the 'value' parameter by clarifying formatting (e.g., for bonds, pass the issuer exactly as printed), warning about trailing security-class words, and giving examples like 'ozempic' or 'metformin'. This goes beyond the schema's own description, which is generic. The 'type' parameter is fully covered by the enum, so no extra semantics are needed.

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 states a clear purpose: resolving user-spoken names to canonical identifiers, with concrete examples of queries like 'What's the ticker for...' and 'is X a subsidiary of Y'. It explicitly says 'Use FIRST whenever you have a name but need an ID,' which distinguishes it from siblings like entity_profile or compare_entities that operate on existing IDs. The supported types and behavior are detailed, making the resource and verb unambiguous.

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?

Provides explicit guidance on when to use: 'Use FIRST whenever you have a name but need an ID.' It also explains input format nuances (e.g., pass only the entity name, not the full noun phrase). However, it does not name specific alternatives or state when NOT to use it (e.g., when you already have an ID). The clear when-to-use outweighs the lack of explicit exclusions.

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

A3.5/5.0
Disambiguation2/5

Many tools are distinct, but the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) heavily overlaps—the beta is explicitly identical to the stable version. The two Chile-specific tools are clear, but the presence of numerous unrelated data tools creates confusion about which tool serves the server's purported purpose.

Naming Consistency2/5

Naming is inconsistent: some tools follow verb_noun snake_case (chile_get_tender, chile_search_tenders), others use plain verbs (ask_pipeworx) or noun_verb patterns (polymarket_arbitrage, entity_profile). Mixed conventions and varying levels of specificity make the set feel uncoordinated.

Tool Count2/5

33 tools is excessive for a server named 'Chile Procurement'—only two tools relate to Chile procurement, while the rest are generic Pipeworx data utilities. The count is not scoped to the server's stated purpose; it appears to be a bundled general-purpose toolkit rather than a focused procurement interface.

Completeness2/5

For Chile procurement, only search and get-detail are provided; there is no ability to list all historical tenders, filter by category or amount, or track bidding. The read-only surface covers basic retrieval but lacks common procurement workflows. The broader data tools are complete individually but irrelevant to the server's domain.

Resources