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Glama

Housing Intel

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 it read-only/idempotent/non-destructive, and the description adds substantial behavior: internal cascading lookups, graceful degradation when GLEIF/OpenFIGI is down, explicit `unresolved` identifiers, ambiguity handling via `figi_candidates`, and source-labelled identifiers. No contradiction with 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 dense and information-rich, with the core directive and examples front-loaded. However, it is one long paragraph with several nested parentheticals, so it could be better structured; it is still appropriately sized for the tool's 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?

Even with no output schema, the description covers return semantics (CIK/ticker/LEI/FIGI, drug RxCUI, `figi_candidates`, `unresolved`, source labels), edge cases (ambiguous names, non-US ISINs, degradation), and input constraints. An agent has enough context to invoke it correctly in almost any expected use case.

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 (100% coverage), the description adds practical semantics: concrete formats (AAPL, 0000320193, CH0038863350, 'ozempic'), the exact name-only instruction for bonds, and the warning that trailing security-class words will fail the FIGI lookup. This meaningfully improves the schema.

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?

States a specific verb and object: resolve a user-spoken name to canonical/official identifiers. The examples (ticker, CIK, LEI, RxCUI) and the two supported types make the scope unmistakable, and the phrase 'identifiers other tools require as input' positions it apart from sibling profile/comparison tools.

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 an explicit trigger: 'Use FIRST whenever you have a name but need an ID.' It does not name sibling alternatives or state when not to use it, so it falls short of the top tier, but the usage context is unambiguous.

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
Disambiguation2/5

Several tools intentionally overlap: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded share the same router, with the beta variant explicitly matching stable behavior right now. entity_profille/recent_changes/compare_entities and the multiple polymarket scanning tools also cover closely related jobs, so an agent must read carefully to avoid picking the wrong variant.

Naming Consistency3/5

All tools use lowercase snake_case, which is a consistent base style. However, the naming grammar is mixed: proper verb_noun tools like compare_entities and validate_claim sit beside noun-phrase/domain tools like housing_market_screen and polymarket_edges, plus the awkward compound case_shiller_metro_compare. The housing_ and polymarket_ prefixes help, but the pattern is not uniform enough for a 5.

Tool Count2/5

41 tools far exceeds the 25+ threshold and the typical well-scoped 3-15 range. Many tools pertyain to Polymarket, npm scanning, llms.txt generation, and memory, which have little to do with Housing Intel, so the count is not earned by the server's stated domain.

Completeness4/5

For the housing domain specifically, the coverage is strong: market snapshot, affordability, employment, mortgage history, rental/property analysis, metro demand, signal scanning, and Case-Shiller comparisons cover the main data needs. The generic ask_pipeworx and deep_research tools also backfill specialized queries. The weakness is scope blur, not obvious missing housing operations.

Resources