Skip to main content
Glama

Httpbin

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 establish read-only, idempotent, non-destructive behavior, but the description adds substantial behavioral detail: cascading through multiple lookup endpoints, graceful degradation when GLEIF/OpenFIGI fail, returning figi_candidates on ambiguity rather than asserting, labeling identifiers by source, and explicitly listing unresolved identifiers. This goes well beyond what annotations convey.

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 the length is largely justified by the tool's multi-source complexity. It is front-loaded with user-intent examples and structured by supported type, though a few parenthetical asides make it harder to scan than necessary.

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 no output schema, the description explains what the tool returns for each entity type: CIK/ticker/LEI/FIGI for companies, RxCUI/ingredient/brand for drugs, ambiguity candidates, and explicit unresolved fields. It also covers input formats and failure modes, giving an agent enough context to invoke it correctly.

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 coverage is 100%, so the baseline is 3, but the description significantly enriches both parameters. It details accepted inputs for company (ticker, CIK, ISIN, name), specifies drug brand/generic examples, explains ISIN-to-LEI resolution, and gives a critical bond-naming caution. This substantially aids correct invocation.

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 explicitly states that the tool resolves a user-spoken name to canonical/official identifiers (CIK, LEI, FIGI, RxCUI) needed by other tools. It enumerates supported types and gives concrete examples like tickers, bonds, and drug names, making its purpose unmistakable and distinguishable from siblings.

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 when-to-use guidance: 'Use FIRST whenever you have a name but need an ID,' and provides numerous natural-language trigger examples. It also warns against passing full noun phrases for bonds, but it does not directly contrast with-related tools like entity_profile or compare_entities.

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

All Httpbin 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

C2.9/5.0
Disambiguation2/5

Many tools serve overlapping purposes: ask_pipeworx, ask_pipeworx_grounded, deep_research, validate_claim, entity_profile, compare_entities, and resolve_entity all perform data lookups with subtle differences. The prediction-market tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.) heavily overlap, and even the HTTP utilities (headers, ip, user_agent, cookies) echo similar request information. Agents will struggle to pick the right tool.

Naming Consistency2/5

Naming is internally inconsistent: some tools use short imperative verbs (get, post, status, delay), others use long descriptive phrases (ask_pipeworx, entity_profile, scan_competitor_ai_presence). There is no common pattern—some are verb+noun, some noun+noun, some proper nouns. The mix of styles makes it hard to predict tool names.

Tool Count2/5

47 tools is excessive for a server named Httpbin, which conventionally should have a handful of HTTP debugging utilities. Most tools are unrelated to HTTP (data lookups, prediction markets, memory, subscriptions), indicating severe scope creep. The count feels bloated and unwieldy.

Completeness2/5

For HTTP debugging, the set is incomplete—missing common methods (PUT, DELETE, PATCH) and error-handling features. For the broader data/proposition-market domain, coverage is fragmented and unclear. The server appears to be a jumble of partially complete feature sets with no coherent domain, leaving obvious gaps in each.

Resources