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Glama

Fda Devices

by io.github.pipeworx-io

Deep Research

deep_research
Read-onlyIdempotent

ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 5,798 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a hop field and a citation_uri — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
depthNoHow many facets to research in parallel: quick=3 (single hop), standard=3 (default; adds a gap-recovery hop that re-angles unanswered facets + a contradictions[] scan across findings), thorough=6 (paid; adds a full iterative hop that chases leads + recovers gaps, plus the contradictions[] scan).
questionYesThe research question, in natural language. Broad/multi-part is fine — decomposition is the point.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / properties / depth / description
      Previous value: -"How many facets to research in parallel: quick=3 (single hop), standard=5 (default; adds a gap-recovery hop that re-angles unanswered facets + a contradictions[] scan across findings), thorough=8 (paid; adds a full iterative hop that chases leads + recovers gaps, plus the contradictions[] scan)."New value: +"How many facets to research in parallel: quick=3 (single hop), standard=3 (default; adds a gap-recovery hop that re-angles unanswered facets + a contradictions[] scan across findings), thorough=6 (paid; adds a full iterative hop that chases leads + recovers gaps, plus the contradictions[] scan)."
  2. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, openWorld), and the description adds substantial behavioral context: auth prerequisites, latency expectations (15-60s, thorough ~90s), output contract (verbatim evidence + confidence + gaps[] + contradictions[]), citation resolvability guarantees, and semantic excerpting of long records. No contradiction with 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 somewhat tangled (convoluted parentheticals, run-on sentences around 'hop' and 'citation_uri'), but nearly every sentence earns its place given the tool's complexity across depth tiers, auth, output contract, and alternatives. Critical constraints are front-loaded before the main description.

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 full burden of explaining return values, and it does so thoroughly: findings packet contents, hop field, citation_uri resolvability, gaps[] honesty guarantee, contradictions[] behavior, depth semantics, auth, and latency. Complex tool, and the description covers everything an agent needs to call it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds value beyond the schema by tying depth tiers to paid-plan access ('thorough' needs a paid plan), clarifying multi-step resolution behavior, and explaining the gaps[]/contradictions[] artifacts that differentiate the depth options. The question-parameter guidance (broad/multi-part is fine) also supplements the schema.

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 specific verb (researches/decomposes/routes), a precise resource (Pipeworx's 1517 structured data sources), and a distinct mechanism (parallel routing to 5,798 tools) with an explicit contrast to open-web search. It also distinguishes itself from ask_pipeworx by scope, so an agent can tell the tools apart without opening their schemas.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-use guidance ('Best for broad/multi-part questions over structured data'), a clear exclusion ('For a single lookup use ask_pipeworx instead'), and a conditional alternative ('If you are not signed in, use ask_pipeworx instead'). Nothing is left to inference.

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 FDA-specific tools are distinct, but they are mixed with many generic Pipeworx tools (e.g., ask_pipeworx variants, deep_research, entity_profile) that have overlapping purposes. This creates significant ambiguity for an agent trying to choose the right tool for FDA-related queries.

Naming Consistency2/5

Tool names follow two inconsistent patterns: FDA tools use 'fda_device_*' (consistent), while generic tools use various patterns like 'ask_pipeworx', 'deep_research', 'remember', etc. The mix of snake_case, camelCase, and descriptive phrases lacks coherence.

Tool Count2/5

With 37 tools, the count is high for what is intended as an FDA devices server. Only 6 tools are directly FDA-related; the rest are generic and dilute the purpose. The scope is mismatched, making the tool count inappropriate.

Completeness3/5

The FDA tools cover key areas: 510k search, adverse events, PMA, recalls, company profiles. However, the server is incomplete for its name because it lacks many tools that a comprehensive FDA devices server would have, and the generic tools don't fill those gaps.

Resources