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Montreal Events

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".

TDQS

A4.8/5.0
Behavior5/5

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

Description discloses embedding model (BGE-base-en), chunking (500-char overlapping windows), similarity measure (cosine), character cap (200K), and truncation behavior. This adds significant context beyond annotations which already indicate read-only, idempotent, etc.

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 single paragraph with clear front-loading of purpose. Every sentence adds value, though could be slightly more concise without losing information.

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?

Completely covers all aspects: what it does, when to use, how it works (embeddings, chunking, scoring), limitations (200K cap, truncation), and return value (passages with offsets). No output schema, but description adequately explains return format.

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 3. Description adds meaningful examples for 'query' and clarifies the 'text' parameter's purpose and limit. The 'limit' parameter's range is also noted, adding value 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 clearly states it performs semantic search inside a fetched record, gives concrete examples (SEC 10-K, article), and distinguishes itself from siblings by mentioning pairing with ask_pipeworx_grounded.

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?

Explicitly says to use when the record is too large for the prompt, explains how it saves context, and provides an alternative usage pattern (fetch with gateway, ground over passages).

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

B3.4/5.0
Disambiguation2/5

The set contains multiple near-duplicate meta-query tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools) and five overlapping Polymarket analysis tools, creating genuine selection ambiguity despite detailed descriptions. The single events tool is distinct, but it is drowned out by a crowd of similar data-research utilities.

Naming Consistency4/5

Almost all tools follow a consistent lowercase snake_case verb_noun pattern (ask_pipeworx, compare_entities, validate_claim, remember, forget). Only 'events' deviates by being a bare noun, but the overall convention is predictable and readable.

Tool Count1/5

32 tools is extreme for a server named 'Montreal Events', and only one tool actually relates to that domain. The remaining 31 form a sprawling, unrelated data-research, prediction-market, and memory toolkit that would overwhelm any agent trying to work with Montréal event data.

Completeness1/5

For the server's stated purpose, the surface is severely incomplete: a single read-only event search with no event details, venue info, categories, or management operations. The Pipeworx tools may cover their own domain thoroughly, but they contribute nothing to the Montreal Events scope.

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