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Ecb

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

A4.6/5.0
Behavior5/5

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

Annotations already show readOnlyHint, idempotentHint. Description adds: returns passages with offsets and scores, uses BGE embeddings, 500-char windows, 200K char cap with truncation flag. No contradictions.

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?

Well-structured with purpose first, then usage, then technical details. Slightly long but each sentence is informative. No wasted words.

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?

No output schema, but description explains return format (passages with offsets and scores) and technical behavior. All essential context is present.

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 covers 100% of parameters with descriptions. Description adds examples for query and reiterates cap for text, adding marginal value beyond 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?

Clearly states the tool does semantic search inside a provided text, with specific examples (SEC 10-K, article). Distinguishes from siblings by mentioning pairing with ask_pipeworx_grounded and use case for large records.

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?

Explicitly says to use when the record is too large for the prompt, saving context. Mentions pairing with another tool for grounding. Lacks explicit 'when not to use' but clear context suffices.

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

A3.9/5.0
Disambiguation2/5

There is significant overlap between tool clusters: ask_pipeworx_beta is currently functionally identical to ask_pipeworx, and polymarket_arbitrage/polymarket_edges/bet_research all target similar opportunity-discovery tasks. While the descriptions are extremely detailed, the sheer number of similar variants makes it hard to reliably pick the right one.

Naming Consistency4/5

Names are consistently snake_case with meaningful prefixes (ask_, ecb_, polymarket_, pipeworx_) and mostly verb-first structure. Minor deviations like entity_profile and ecb_hicp_inflation are noun-first, but they do not break the overall pattern.

Tool Count2/5

35 tools is well beyond the well-scoped 3–15 range, and the surface feels bloated with redundant meta-tools (ask_pipeworx variants, deep_research, discover_tools, suggest_questions) and peripheral utilities like generate_llms_txt and scan_dependency. Many entries could be consolidated without losing function.

Completeness4/5

For the server's broad data-research domain, coverage is thoughtful and full: query, grounded answer, validation, entity resolution, profiling, comparative analysis, subscription lifecycle, memory, and feedback are all represented. Minor gaps exist (e.g., no direct raw ECB flow browser beyond generic SDMX) but nothing blocking.

Resources