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Openparliament Ca

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

A4.7/5.0
Behavior5/5

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

Adds significant behavioral context beyond annotations: uses BGE-base-en embeddings, cosine over 500-char windows, 200K char cap with truncation flag, and each passage includes offset for verification. 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?

Four sentences, no fluff. Front-loaded with purpose, followed by usage guidance, sibling pairing, and technical details. Every sentence earns its place.

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 the schema, annotations, and no output schema, the description covers what the output looks like (passages with offsets and scores), technical details (embedding model, windowing, truncation), and usage context. Fully sufficient for an AI agent.

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% with good property descriptions. The description adds extra value with query examples and context for the 'text' parameter, exceeding the baseline of 3.

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 clearly states it performs 'Semantic search INSIDE a fetched record' and provides concrete examples (SEC 10-K, article). It distinguishes from the sibling tool ask_pipeworx_grounded by explaining how they pair together.

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 'Use when the record is too big to cram into the prompt' and mentions pairing with ask_pipeworx_grounded. Could be clearer on when not to use, but the guidance is strong.

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

B3.1/5.0
Disambiguation2/5

The tool set mixes two distinct domains: 8 Canadian Parliament tools and 30 Pipeworx data tools. Within each domain, tools are somewhat distinct, but the overall mix creates confusion as agents cannot tell if a tool is for parliament or general data lookup.

Naming Consistency2/5

OpenParliament tools follow a consistent verb_noun pattern (list_*, get_*), but Pipeworx tools use varied conventions (e.g., 'remember', 'discover_tools', 'ask_pipeworx'). The lack of a unified naming scheme across the set reduces predictability.

Tool Count2/5

38 tools is excessive for a Canadian Parliament server. The core parliament tools (8) are well-scoped, but the addition of 30 unrelated Pipeworx tools makes the count bloated and inappropriate for the stated domain.

Completeness1/5

For the Canadian Parliament domain, coverage is adequate but lacks topic search and detailed legislative history. However, the server is dominated by Pipeworx tools, which are out of scope, making the overall surface severely incomplete for the implied purpose.

Resources