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Glama

Spain Tenders

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

A4.7/5.0
Behavior5/5

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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses substantial behavior: internal cascading lookups, graceful degradation when GLEIF/OpenFIGI is unavailable, explicit unresolved identifiers, and returning figi_candidates rather than asserting an ambiguous match. These details meaningfully shape invocation and result interpretation.

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 the length is justified by the breadth of supported entity types and edge cases. The purpose and usage statement is front-loaded; later details are organized by supported type and degradation behavior, making the content navigable despite its size.

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 compensates by explaining what the tool returns: labeled identifiers, unresolved entries, and figi_candidates for ambiguous names. It also covers fallback behavior, enrichment limitations, and parameter constraints, so an agent has enough context to call it correctly in varied scenarios.

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 already 100%, but the description adds significant value: accepted input forms include ticker, CIK, ISIN, or name for companies; ISIN resolves to the issuing legal entity; drug accepts brand/generic names; and the bond example explains exactly what to avoid passing. This goes well beyond the schema descriptions.

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 queries and states the core action: resolving a user-spoken name to canonical/official identifiers. It distinguishes the tool from siblings like entity_profile and compare_entities by positioning it as the 'name to ID' conversion step needed before other tools can be used.

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 explicitly says 'Use FIRST whenever you have a name but need an ID,' which is clear guidance for the primary use case. It does not enumerate when not to use specific sibling tools like entity_profile, but it provides enough contextual direction to route an agent correctly.

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

A3.7/5.0
Disambiguation2/5

Several clusters overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical routers (the beta currently matches stable exactly), and the Polymarket tools all circle around edge/arbitrage detection with fuzzy boundaries. The Spain tender tools and utility tools are distinct, but the overlapping clusters are enough to cause misselection.

Naming Consistency3/5

Names are uniformly snake_case and some families share clear prefixes (es_tender_*, ask_pipeworx_*, polymarket_*). However, conventions are mixed: verb-led names like compare_entities and subscribe sit alongside noun phrases like entity_profile and bet_research, so there is no consistent verb_noun pattern.

Tool Count2/5

34 tools is already in the heavy range, but the bigger problem is scope: a server named 'Spain Tenders' ships 34 tools, only 3 of which are actually Spanish-procurement tools. The rest are a broad Pipeworx/Polymarket/utility toolkit, making the count inappropriate for the declared purpose.

Completeness3/5

The three es_tender_* tools cover the core discovery workflows: keyword search, recent notices, and status filtering with budgets, deadlines, and URLs. Notable gaps remain, though: no tender detail-by-id tool, no tender-specific subscription/alerting, and no explicit region, CPV, or date-range filters.

Resources