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Idf 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

A4.5/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint, destructiveHint), the description reveals embedding model (BGE-base-en), windowing (500-char overlapping windows), similarity (cosine), and truncation behavior (200K char cap with flag). This adds substantial behavioral context.

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, tightly written: 1) defines purpose, 2) use case, 3) pairing with sibling, 4) technical details. No redundancy; front-loaded with the core action.

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?

Even without an output schema, the description covers input (text+query), output (passages with offsets and scores), use case, pairing, technical constraints (cap, truncation), and limits. It is complete for an agent to understand the tool's behavior.

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 coverage is 100%, so baseline is 3. The description elaborates on usage (e.g., example queries for query param) but does not add significant meaning beyond the schema's param descriptions.

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 'Semantic search INSIDE a fetched record', specifying a specific verb (search inside) and resource (fetched record). It distinguishes from siblings by mentioning ask_pipeworx_grounded as a pairing tool, clarifying the tool's 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?

Explicit guidance: 'Use when the record is too big to cram into the prompt' and 'Pairs with ask_pipeworx_grounded' to ground over passages. Does not explicitly state when not to use, but contextually implies alternatives.

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

Many tools have overlapping purposes (e.g., three ask_pipeworx variants, multiple Polymarket analysis tools, and several entity-focused tools). Agents may struggle to select the correct tool for tasks like querying data or analyzing prediction markets.

Naming Consistency4/5

All tool names use snake_case, and most follow a verb_noun pattern (e.g., ask_pipeworx, compare_entities). A few names like ai_visibility_check are slightly less conventional, but overall the naming is consistent.

Tool Count3/5

With 32 tools covering a broad range of data services, the count is on the high side but still manageable. However, the server name 'Idf Events' is misleading, as only one tool relates to events in Paris.

Completeness4/5

The tool set covers core workflows for the Pipeworx platform: data querying, research, comparisons, subscriptions, memory, and feedback. Minor gaps exist, but most user needs are addressed.

Resources