Skip to main content
Glama

Bls

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.

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already mark it read-only/idempotent, but the description adds substantial behavioral detail: graceful degradation when LEI/FIGI sources fail, ambiguous matches returning figi_candidates instead of asserting, unresolved identifiers being explicitly listed, and internal cascading through multiple endpoints. This goes well beyond the annotations.

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 but densely informative, front-loaded with user intents and organized by SUPPORTED TYPES. Each parenthetical adds operational value, though the wall of text could be broken up for easier scanning.

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?

With no output schema, the description carries the burden of explaining return behavior, and it does: it names returned fields (CIK, ticker, company_name, LEI, FIGI, RxCUI, ingredient, brand), the ambiguous case (figi_candidates), unresolved identifiers, and source attribution. For a complex resolution tool, this is remarkably complete.

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?

Schema covers the parameters, but the description adds crucial semantics: 'Pass the ENTITY NAME ONLY,' the distinction between ticker/CIK/name for companies, and the warning that trailing security-class words break FIGI matching. This meaningfully clarifies how to construct the value beyond the schema's bare examples.

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?

States a precise verb+resource: resolves user-spoken names to canonical/official identifiers needed by other tools. It names concrete use cases ('find the CIK for...', 'who owns X') and supported entity types, clearly distinguishing it from siblings like compare_entities and entity_profile.

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?

Gives an explicit usage rule: 'Use FIRST whenever you have a name but need an ID.' It also lists common query phrasings that should trigger this tool. It does not explicitly name sibling alternatives or say when NOT to use it, so it stops short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Bls toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation2/5

The ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded trio are nearly indistinguishable, with beta currently behaving identically to the stable version. Entity_profile, recent_changes, and compare_entities also overlap heavily as multi-source company research tools, and the six polymarket tools create additional boundary confusion.

Naming Consistency2/5

Most tools use snake_case, but there is no consistent verb_noun pattern: some are imperative phrases (ask_pipeworx, bls_get_series, resolve_entity), while others are noun phrases (entity_profile, pipeworx_feedback, polymarket_edges, recent_alerts). Even within the bls_* family, bls_latest breaks the verb pattern established by bls_get_series and bls_search.

Tool Count2/5

35 tools is well above the 25+ threshold for 'too many', and the server is named Bls yet only four tools actually serve BLS data. Most of the remaining tools cover unrelated domains like Polymarket arbitrage, memory, feedback, and general Pipeworx routing, making the count feel inflated for the apparent purpose.

Completeness3/5

The four BLS-specific tools cover search, browse, historical series fetch, and latest value, but there is no multi-series fetch or series metadata detail, which is a notable gap for a BLS-focused server. The broader Pipeworx toolset is extensive, but the lack of a coherent stated domain makes completeness hard to evaluate as a unified surface.

Resources