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Glama

Bamboohr

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

A4.8/5.0
Behavior5/5

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

The description goes far beyond the read-only and idempotent annotations by disclosing multi-source fallbacks (EDGAR, GLEIF, OpenFIGI), graceful degradation if external sources fail, how ambiguity is handled (returns figi_candidates, asserts nothing), explicit unresolved identifiers, and edge cases like ISIN-to-LEI mapping. This is rich behavioral context that annotations alone do not provide.

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

Conciseness5/5

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

The description is long but every sentence delivers distinct value—usage trigger, supported types, cross-source details, degradation behavior, and parameter nuances. It is well-organized with clear topic breaks, and the most actionable guidance ('Use FIRST...') appears up front.

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 proactively explains what the tool returns (identifiers, labels, unresolved, figi_candidates) and how it handles failures. With a complex multi-endpoint tool, this level of detail ensures an agent knows what to expect and how to react. Nothing essential 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?

Although the schema covers 100% of parameters, the description adds critical usage semantics beyond field definitions: explains the exact input format for company names (e.g., issuer as printed, not the full noun phrase), gives examples (AAPL, CIK, ISIN), and warns about trailing security-class words. This significantly helps an agent call 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 states a specific action ('resolve a user-spoken NAME to the canonical/official identifiers'), names the resource types (company, drug), and clearly distinguishes itself from sibling tools that profile or compare entities. It covers supported input forms and output identifiers, leaving no ambiguity about what the tool does.

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 explicitly says 'Use FIRST whenever you have a name but need an ID' and elaborates on when each entity type is appropriate. It does not explicitly state when NOT to use it or name alternative tools, but the 'FIRST' directive and clear input requirements give strong contextual guidance.

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

B3.4/5.0
Disambiguation3/5

The five BambooHR tools are distinct, but the set is dominated by overlapping Pipeworx/Polymarket search and research tools: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve similar lookup purposes, and polymarket_edges/polymarket_edge_tracker/polymarket_arbitrage/polymarket_fill_risk/polymarket_kalshi_spread occupy closely related prediction-market territory. Descriptions help differentiate them, but an agent could easily select the wrong one.

Naming Consistency2/5

Naming conventions are heavily mixed: camelCase (ai_visibility_check, ask_pipeworx_grounded), snake_case with varying verb positions (bamboohr_get_directory, list_subscriptions, resolve_entity), and domain-prefixed families that do not share a consistent pattern. Some tools are named by action (bet_research, compare_entities) rather than resource-object style, and the Pipeworx meta-tools follow a different convention than the BambooHR tools.

Tool Count2/5

36 tools is excessive for a server ostensibly named Bamboohr, and the vast majority are unrelated to HR—they cover general data research, prediction markets, AI visibility, and memory storage. The BambooHR-specific surface is only 5 tools buried inside a much larger third-party platform, making the count disproportionate to the stated server purpose.

Completeness2/5

The BambooHR domain is severely under-covered: read operations exist for directory, employees, employee files, and time off, but there are no create/update/delete operations, no time-off request management, no org chart access, no payroll or benefits tools, and no employee lifecycle workflows. Meanwhile, the many non-HR tools are extensive for their own domains but do not fill the obvious HR gaps.

Resources