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Glama

Census Trade

by io.github.pipeworx-io

Search Within a Source

search_within
Read-onlyIdempotent

Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe document text to search inside (max ~200K chars).
limitNoMax passages to return (1-20, default 5).
queryYesNatural-language query — what passages do you want? E.g. "supply-chain risk", "fiscal year 2024 revenue", "drug interactions with warfarin".

Schema Changelog

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

  1. Added

TDQS

A4.4/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint=false), the description adds critical behavioral details: uses BGE-base-en embeddings, cosine similarity on 500-char overlapping windows, 200K char cap with truncation flag, and returns character offsets and similarity scores. 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Five sentences, each earning its place. The first sentence front-loads the purpose, followed by usage guidance, technical details, and constraints. No filler or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

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 by explaining return values (passages, offsets, similarity scores). It covers constraints (character cap, truncation), embedding model, and pairing suggestion. Minor omissions: no explicit mention of whether multiple queries are supported or if text encoding is required.

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

Parameters3/5

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

Schema description coverage is 100%, so baseline is 3. The description adds helpful query examples but mostly restates what the schema already provides for `text` and `limit`. It does not add substantial new semantic meaning beyond 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?

The description uses a specific verb-resource combination ('semantic search inside a fetched record'), gives concrete examples (SEC 10-K body, article), and distinguishes itself from sibling tools like `ask_pipeworx_grounded`. It clearly communicates what the tool does and its niche.

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 explicitly advises using this tool when 'the record is too big to cram into the prompt' and mentions pairing with `ask_pipeworx_grounded`. While it doesn't explicitly list when not to use it, the context is clear enough for an agent to make appropriate decisions.

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

All Census Trade toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

There is substantial overlap among the question-routing tools: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and ask_pipeworx_grounded, deep_research, and validate_claim all funnel natural-language queries into similar source lookups. ai_visibility_check and scan_competitor_ai_presence also cover nearly the same capability, making tool selection genuinely ambiguous.

Naming Consistency3/5

All names are snake_case and many follow a verb_noun pattern (census_exports, list_subscriptions, validate_claim), but the convention drifts with noun-first names like entity_profile, pipeworx_trending, and recent_alerts, and bare verbs like remember, recall, forget, and subscribe. It is readable but not a tight, predictable pattern.

Tool Count2/5

35 tools is too many for a server named 'Census Trade' when only 4 of them actually relate to Census trade data. The remaining 31 form an unrelated general-purpose data, prediction-market, memory, and subscription toolkit, making the surface feel bloated and badly scoped relative to the server's apparent purpose.

Completeness3/5

The Census trade core covers exports, imports, trade balance, and monthly trends, which handles the central queries, but there is no HS-code catalog, country metadata, or state/port-level breakdown, leaving notable gaps for a trade-data domain. If judged as the broad Pipeworx gateway it appears to actually be, coverage is richer, but that contradicts the server name and weakens overall coherence.

Resources