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Glama

Amplitude

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

A4.9/5.0
Behavior5/5

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

The description goes far beyond the readOnly/openWorld/idempotent annotations by disclosing internal cascading lookups, graceful degradation when GLEIF or OpenFIGI is unavailable, the use of figi_candidates for ambiguous matches, explicit unresolved output fields, and source labeling for every identifier. This gives an agent a strong model of what the tool will and will not return.

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 no filler, but it is structured as one long paragraph with many nested parentheticals. It front-loads the core purpose and usage rule, and every clause adds value, but splitting the entity types or edge cases into clearer sections would improve scannability.

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?

For a two-parameter tool with no output schema, the description is remarkably complete: it covers accepted inputs, resolution behavior, ambiguity handling, unresolved identifiers, fallback behavior, and even the internal cascade that justifies why this tool replaces multiple lookups. An agent has enough context to invoke it correctly in most realistic 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?

Although the schema already documents both parameters at 100% coverage, the description adds crucial semantic guidance: pass only the entity name, not the full noun phrase, and explains why trailing security-class words break FIGI matching. It also enriches the enum values with concrete examples ('AAPL', '0000320193', 'ozempic').

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 precise verb and resource: resolve a user-spoken name to canonical/official identifiers. It names the supported entity types (company, drug) and the identifier systems returned (CIK, ticker, LEI, FIGI, RxCUI), and it clearly distinguishes itself from sibling tools like entity_profile by positioning itself as the first step when a name but not an ID is available.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'Use FIRST whenever you have a name but need an ID,' which is direct guidance on when to invoke the tool. It also provides per-type input expectations (ticker/CIK/ISIN/name for company; brand or generic name for drug) and clarifies what input forms are accepted, including edge cases like issuer names for bonds.

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

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta (currently identical), ask_pipeworx_grounded, deep_research, and validate_claim all answer natural-language questions over the same underlying data sources. ai_visibility_check and scan_competitor_ai_presence also overlap as single vs. comparative variants. The detailed descriptions help, but the boundaries between the query/research tools remain genuinely ambiguous for an agent.

Naming Consistency2/5

No consistent global naming convention. There are prefix families (amp_*, pipeworx_*, polymarket_*) but within them the structure varies (amp_get_events vs amp_user_search; ask_pipeworx vs polymarket_fill_risk), and many tools are bare verbs or noun phrases (remember, recall, forget, bet_research, search_within, recent_alerts). The mix of verb-first and noun-first names with irregular prefixes makes predicting tool names unreliable.

Tool Count2/5

36 tools is too many for the server's nominal purpose: only 5 of them (amp_*) relate to Amplitude analytics, while the other 31 form a sprawling all-in-one data/research/prediction-market platform. Even accepting that broader scope, many tools could be consolidated (ask_pipeworx_beta duplicates ask_pipeworx, several polymarket tools are specialized but still numerous), making the count feel padded rather than focused.

Completeness3/5

The Pipeworx side is thorough: lookups, grounded verification, deep research, entity profiles, comparisons, subscriptions, and memory cover most of that domain well. However, the Amplitude analytics side is thin — it only queries events, active users, retention, and user activity, with no way to manage projects, cohorts, event definitions, or user properties. There is also no general web search tool and no direct database/SQL exploration, leaving notable gaps for the advertised all-in-one positioning.

Resources