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Codestats

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?

Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint), the description adds rich behavioral context: it explains graceful degradation when GLEIF/OpenFIGI are unavailable, how multi-match instruments yield `figi_candidates` rather than an assertion, that unresolved identifiers are explicitly listed rather than omitted, and that a single call cascades over several lookup endpoints. This substantially exceeds what annotations alone communicate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, extremely dense run-on paragraph that mixes purpose, supported types, edge cases, and degradation behavior. While it is front-loaded with the purpose, the sheer length (over 300 words) and sprawling parentheticals make it hard to scan. A more structured layout (bullets for supported types, subsections for caveats) would aid an agent's parsing. This verbose structure costs clarity.

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?

The description is extraordinarily complete given the tool's complexity. It covers both supported types, input formats, edge cases (ambiguous bonds, non-US issuers, unsupported securities), fallback behavior, and explains what the tool returns and how it handles unresolved lookups. No output schema exists, but the text compensates thoroughly. There is nothing an agent needs to know to invoke this tool correctly that is missing.

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?

The schema covers both parameters (type enum, value with examples), but the description goes far beyond: it elaborates the semantics of each value, including the bond-issuer formatting caveat, the distinction between brand/generic drug names, and the behavior when a value is ambiguous. It also clarifies what 'type' means with detailed explanations of the company and drug branches. This adds significant meaning 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 opens with concrete example questions ('What's the ticker for…', 'find the CIK for…') then states the tool's core job: resolving a user-spoken name to canonical/official identifiers. It explicitly distinguishes itself from siblings by saying 'Use FIRST whenever you have a name but need an ID.' The purpose is unmistakable and immediately actionable.

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 gives strong 'when to use' guidance: 'Use FIRST whenever you have a name but need an ID.' It also explains when the tool is especially needed, such as for non-equity instruments without tickers, and provides specific input-format warnings (e.g., pass the exact issuer name, not the full noun phrase). It doesn't explicitly state 'don't use if you already have the ID,' but that is clearly implied by the 'use FIRST' directive. A minor gap separates this from the top score.

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
Disambiguation3/5

Most tools have distinct, well-described purposes, but clusters like ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded and the five polymarket_* tools have overlapping scopes that could cause misselection. The beta currently behaves identically to the stable router, and `recent` vs `recent_changes` vs `recent_alerts` are confusingly similar names for different domains.

Naming Consistency2/5

All names use lowercase underscores, but there is no consistent verb_noun pattern: some are verbs (ask, compare, generate), some are nouns (entity_profile, recent, user), and some are adjectives (deep_research). There are coherent subfamilies (subscribe/unsubscribe/list_subscriptions, remember/recall/forget), but the overall naming is a mix of conventions.

Tool Count2/5

33 tools is well above the 25+ threshold, making the surface heavy and hard to navigate. Many are highly specialized meta-tools (e.g., five Polymarket analyzers) that could be consolidated.

Completeness2/5

For a server named 'Codestats', only `recent` and `user` address coding stats, leaving major gaps in what that name implies. The broader data/research/betting capabilities are fairly rich, but the server's stated identity is under-served and there's no clear lifecycle coverage for any single domain.

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