Skip to main content
Glama

Peeringdb

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?

The description adds substantial behavioral context beyond the annotations. It explains the cross-source identity spine (CIK, ticker, LEI, FIGI), graceful degradation when GLEIF/OpenFIGI are unavailable, ambiguous-match behavior returning figi_candidates instead of asserting a wrong answer, explicit unresolved identifiers, and the fact that each call cascades through multiple internal endpoints. This far exceeds the safety hints already provided by readOnlyHint, openWorldHint, idempotentHint, and destructiveHint.

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 almost every sentence carries useful operational detail, including edge cases, fallback behavior, and source attribution. It is front-loaded with examples and the 'Use FIRST' instruction, then organizes supported types with sub-details. Some redundancy and parenthetical density exists, but given the complexity of the tool, the length is largely justified. It is not as lean as it could be, but it is far from bloated.

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?

Given the tool's complexity, the absence of an output schema, and the two-parameter schema, the description is remarkably complete. It explains return behavior implicitly by describing figi_candidates for ambiguous matches, the unresolved list, source-labelled identifiers, and fallback behavior when upstream services fail. It even tells the agent what kind of input to pass (entity name only) and why. No critical operational aspect appears missing for an agent to correctly select and invoke this tool.

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 already covers both parameters 100%, the description adds substantial meaning beyond the schema. It explains what values mean in practice for each type (e.g., ticker vs. CIK vs. name for company; brand vs. generic for drug), gives concrete examples, explains the bond issuer-name matching caveat, and clarifies the behavior when multiple instruments match. This is a case where the description elevates the parameter semantics well above the schema baseline.

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, actionable purpose: resolving a user-spoken NAME to canonical/official identifiers that other tools require as input. It goes further with concrete examples ('ticker for…', 'CIK for…', 'LEI for…') and explicitly distinguishes the two supported entity types, company and drug, making it clear what the tool does and how it differs from ordinary search. The phrase 'Use FIRST whenever you have a name but need an ID' solidifies its role among sibling tools.

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?

Strong usage guidance is present: 'Use FIRST whenever you have a name but need an ID' gives an explicit trigger condition. The description also provides detailed input-format guidance, including what to pass for bonds and what not to pass ('never the question's full noun phrase'). However, it does not name specific sibling alternatives or explicitly state when NOT to use this tool, so it falls just 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 Peeringdb 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

A3.5/5.0
Disambiguation2/5

Many tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve question-answering/discovery; polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread, and bet_research all analyze prediction markets; ai_visibility_check and scan_competitor_ai_presence overlap heavily. Only the three PeeringDB search tools are clearly distinct.

Naming Consistency2/5

Naming mixes verb-led snake_case (search_networks, validate_claim, subscribe) with noun-phrase tools (entity_profile, bet_research, recent_alerts) and inconsistent prefixes (ask_pipeworx vs ask_pipeworx_beta vs ask_pipeworx_grounded; polymarket_arbitrage vs polymarket_fill_risk vs polymarket_kalshi_spread). No consistent verb_noun pattern is present.

Tool Count2/5

34 tools is heavy for a coherent surface, especially since the server is named 'Peeringdb' but only 3 of 34 tools relate to PeeringDB. The Pipeworx/Prediction-market/memory/AI-visibility tools form several distinct sub-domains that would be better split into separate servers or consolidated.

Completeness2/5

For PeeringDB, only search_exchanges/facilities/networks exist—no get-by-id, no facility/network details beyond search results, and no read/update operations. For the broader Pipeworx domain, coverage is fragmented: many meta-tools overlap while some obvious operations (e.g., updating a saved memory, deeper entity relationships) are missing. The domain is poorly scoped, making completeness hard to assess.

Resources