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Reddit

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. Added

TDQS

A4.7/5.0
Behavior5/5

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

Even with readOnlyHint and idempotentHint annotations, the description adds substantial behavioral nuance: ambiguous matches return figi_candidates rather than asserting, unresolved identifiers are explicitly listed under 'unresolved', and GLEIF/OpenFIGI enrichment degrades gracefully while EDGAR identifiers still return. This is strong transparency 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 and dense with nested parentheticals, but the complexity of the tool justifies most of it. It is front-loaded with user intents and the core purpose. There is some repetition between 'cascades through several lookup endpoints' and 'replaces 2-3 manual lookups,' so it is slightly heavier than strictly 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?

With no output schema, the description compensates well by covering key return behaviors: source-labeled identifiers, figi_candidates for ambiguity, an unresolved list, RxCUI citation links, and LEI ownership information. For a two-parameter tool with this level of internal complexity, the description is complete enough for an agent to call 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%, but the description goes much further: it documents accepted input formats for value (ticker, CIK, ISIN, company name, brand/generic), gives an ISIN-to-LEI example, and warns against including trailing security-class words for bond issuer names. The enum semantics for 'company' and 'drug' are also substantially enriched.

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 opens with a precise operation: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It names the supported entity types and positions the tool as an identifier-resolution step, which distinguishes it from research or profile-oriented 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?

It gives an explicit directive: 'Use FIRST whenever you have a name but need an ID,' plus concrete query-pattern examples and a note that it 'replaces 2-3 manual lookups.' It does not name alternatives or state exclusions, but the usage context is clear.

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

A3.5/5.0
Disambiguation2/5

The set has several near-duplicate tools: ask_pipeworx and ask_pipeworx_beta are explicitly designed to be identical right now, and ask_pipeworx_grounded, deep_research, and bet_research all overlap on routed data lookup. The three Reddit tools are distinct, but they are dwarfed by a large cluster of unrelated Pipeworx/Polymarket tools, making the actual purpose of the set hard to pin down.

Naming Consistency2/5

Naming conventions are wildly mixed: some tools use verb_noun (get_post, subscribe, suggest_questions), some use noun phrases (entity_profile, recent_alerts, pipeworx_trending), and some use ad-hoc names (ask_pipeworx, bet_research, scan_dependency). Within sub-families (polymarket_*, ask_pipeworx_*) names are consistent, but overall there is no single predictable pattern.

Tool Count1/5

34 tools is excessive for a server named 'Reddit' when only 3 of them (get_post, get_subreddit, search_posts) actually relate to Reddit. The remaining 31 tools cover unrelated domains like Pipeworx data routing, Polymarket betting, memory management, and npm dependency scanning—an extreme scope mismatch for the advertised server name.

Completeness1/5

The Reddit surface is severely incomplete: it offers read-only post/subreddit/search functionality with no create, edit, delete, vote, comment, or user-profile operations. Conversely, the Pipeworx tooling is over-complete relative to the 'Reddit' name, covering data lookups, prediction markets, subscriptions, and memory—none of which belong in a Reddit server.

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