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Glama

Scrapingdog

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 read-only/idempotent annotations, the description reveals useful behavioral details: it cascades across EDGAR, GLEIF, and OpenFIGI internally; LEI/FIGI enrichment degrades gracefully; and ISINs resolve to the issuing legal entity, including non-US issuers. These details materially shape agent expectations.

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 information-dense and front-loads purpose and usage, but it is written as long run-on sentences with nested parentheticals, making it harder to scan than an ideally structured definition. Every sentence earns its place, but the structure could be tightened.

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 there is no output schema, the description compensates by listing the identifiers returned per type, accepted input forms, availability degradation, and an internal cascade note. It gives an agent enough behavioral context to call resolve_entity correctly for both supported types.

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?

Even though the schema already covers 100% of parameters, the description adds meaning beyond it: ISIN is an accepted company input, non-equity instruments resolve via FIGI name search, and the description's instruction to pass the issuer exactly as printed prevents a common noun-phrase mistake. This is exactly the semantic context an agent needs.

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 verb-resource relationship: it resolves user-spoken names to canonical/official identifiers that other tools need as input. It also enumerates supported types (company, drug) and output identifiers, which makes it easy to distinguish from sibling tools like entity_profile or compare_entities.

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 a direct trigger: 'Use FIRST whenever you have a name but need an ID.' It does not explicitly name alternatives or list when-not-to-use conditions, but the first-use directive and supported-type scope provide clear context for selecting this tool.

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

Multiple tools have unclear boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical, ai_visibility_check overlaps with scan_competitor_ai_presence, and the six Polymarket tools all orbit the same edge-detection concept. Descriptions are detailed, but an agent would frequently have to read long text to decide which near-overlapping tool to call.

Naming Consistency2/5

Naming is mostly snake_case but semantically inconsistent: some names are verb-led (ask_pipeworx, validate_claim, remember), some noun-led (polymarket_edges, entity_profile), and some use a vendor prefix (scrapingdog_scrape, scrapingdog_amazon_product). The polymarket_edges vs polymarket_edge_tracker singular/plural pairing adds further confusion.

Tool Count2/5

34 tools is above the 25+ threshold and the count is not justified by a single clear purpose. The server is named Scrapingdog but most tools are unrelated Pipeworx research, memory, subscription, and prediction-market functionality, making the set feel overstuffed and unfocused.

Completeness3/5

The data-research and subscription/memory lifecycles are fairly complete, with create/read/delete coverage for those areas. However, relative to the Scrapingdog scraping identity, the surface is thin: only three scraping tools exist, and there is no direct way to fetch a Pipeworx record by URI or manage scraped-data artifacts.

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