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Glama

Alpaca

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 annotations (readOnly, openWorld, idempotent, non-destructive), the description discloses important behavioral traits: graceful degradation ('if GLEIF or OpenFIGI is unavailable, the EDGAR identifiers still return'), ambiguous-result handling ('figi_candidates' and explicit 'unresolved' fields), and internal cascading across multiple lookup endpoints. It also explains edge cases like ISIN-to-LEI resolution for non-US issuers.

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 it front-loads the core purpose and usage rule before diving into edge cases. While some parentheticals are heavy and could be structured more cleanly, nearly every sentence contributes operational context, including unsupported instrument types and degradation behavior. It is verbose but justified 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?

For a read-only resolution tool with no output schema, the description covers supported entity types, input formats, identifier sources, fallback behavior, ambiguity handling, and non-US/non-equity coverage. It also names the return fields (CIK, ticker, LEI, FIGI, RxCUI, ingredient, brand, citation, unresolved). An agent has enough detail to select and call the tool correctly across the main intended 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 schema coverage is 100%, the description adds crucial meaning beyond the schema: it documents ISIN as an accepted company input (the schema only mentions ticker, CIK, or name), warns against passing full noun phrases ('never the question's full noun phrase'), and explains the exact entity-name formatting expectation for bonds. These are high-value operational details an agent needs to invoke the tool correctly.

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-phrase examples and then states a specific job: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It clearly identifies the resource (names → IDs) and the two supported types (company, drug), and positions itself distinctly from siblings like entity_profile by framing it as the first-step lookup 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?

The description gives explicit usage guidance: 'Use FIRST whenever you have a name but need an ID.' It also explains that one call replaces multiple manual lookups and covers cases like non-ticker bonds and non-US issuers. It does not explicitly name sibling alternatives or state when not to use it, but the use context is unmistakable.

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 tools occupy heavily overlapping territory: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, six Polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread, bet_research) circle the same opportunity-finding domain, and entity_profile/recent_changes/compare_entities/validate_claim are all company-research flavored. While each pair has a stated distinction, an agent would frequently need to read deep into long descriptions to avoid misselection.

Naming Consistency3/5

All names use snake_case, so there is no style chaos, but the pattern is inconsistent: some tools are verb-first (remember, validate_claim, resolve_entity), some are noun-first (entity_profile, recent_changes, alpaca_snapshot), and many use domain prefixes (ask_pipeworx*, polymarket_*, pipeworx_*) that overlap with bare verb-noun tools. The mix is readable but does not give a predictable clue to what a tool does.

Tool Count2/5

34 tools is well above the 25+ threshold, and the count is inflated by redundant/overlapping surfaces: a beta duplicate, six overlapping Polymarket tools, and multiple meta/discovery tools (discover_tools, suggest_questions, pipeworx_trending). Only three tools actually relate to the server's stated Alpaca identity, making the scope feel sprawling rather than well-scoped.

Completeness3/5

The research/query surface is broad and covers a lot of workflows (single lookups, grounded verification, deep research, entity profiles, comparisons, change feeds). But there are notable gaps: pipeworx:// citation URIs are promised as resolvable yet no read/fetch tool exists, subscriptions cannot be updated (only created/cancelled), and the Alpaca tools are read-only with no account/order surface despite the server name. These are workaround-able but would cause friction or dead ends.

Resources