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Glama

Passive Aggression

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?

The description goes far beyond the annotations by disclosing graceful degradation when GLEIF/OpenFIGI are unavailable, the ambiguous-match behavior that returns figi_candidates, the explicit unresolved field, source labeling for identifiers, and the internal multi-endpoint cascade. This gives the agent a detailed behavioral model.

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 front-loaded with query patterns and the critical 'use FIRST' directive, and almost every clause adds value. However, the company branch is one very dense, nested parenthetical block that would be easier to scan if restructured into bullets or shorter sentences.

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?

Despite having no output schema, the description covers supported types, input constraints, ownership resolution, ambiguous results, unresolved identifiers, and fallback behavior. An agent has enough context to invoke the tool correctly across company and drug lookups.

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 covers both parameters, the description adds substantial meaning: accepted company inputs, ISIN-to-LEI behavior, the ENTITY NAME ONLY warning, and the concrete pitfall about trailing security-class words. This materially improves parameter understanding beyond 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?

The description states a specific verb and resource: 'resolve a user-spoken NAME to the canonical/official identifiers.' It also frames the tool as the first step when an ID is needed, making clear it is an identity-lookup gateway rather than a profiling or comparison tool.

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?

Explicit guidance is given: 'Use FIRST whenever you have a name but need an ID.' It also clarifies which entity types and input forms are supported. However, it does not explicitly name sibling alternatives or state when not to use the tool, so it stops short of a full 5.

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

A3.8/5.0
Disambiguation2/5

Multiple tools have overlapping purposes, particularly the various data query tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, bet_research, etc.) that share similar functionality with subtle differences. The detailed descriptions help but the boundaries are not always clear, making it hard for an agent to reliably select the correct tool.

Naming Consistency3/5

The naming follows a mix of patterns: some tools use consistent verb_noun (subscribe, unsubscribe, remember, recall) but others are inconsistent (ask_pipeworx vs deep_research vs entity_profile). The main data tools have a common prefix but diverge in style, and the presence of tools like passive_aggression_detect and generate_llms_txt adds further inconsistency.

Tool Count2/5

With 32 tools, the server feels bloated. Many tools could be consolidated (e.g., the ask_pipeworx variants, the Polymarket tools). The inclusion of tangential tools like passive_aggression_detect and generate_llms_txt suggests scope creep. A typical well-scoped server would have 10-15 tools.

Completeness3/5

The server covers a wide range of data access and some auxiliary functions (memory, subscriptions, feedback), but there are notable gaps like user authentication and data visualization. The addition of an unrelated sentiment analysis tool makes the surface feel incomplete for a focused data server.

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