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Madrid 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

A4.7/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses concrete behavior: BGE-base-en embeddings, 500-char overlapping windows, 200K char cap with truncation flagged, and every passage carrying an offset for verbatim verification. These detail the operation's mechanics and edge cases without contradicting 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?

The description is dense but every sentence earns its place: definition, use case, integration, and technical details. It is front-loaded with the core action, and no fluff or repetition exists.

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?

With no output schema, the description fills the gap by explaining return content (passages, offsets, similarity scores). It also covers the input cap, truncation behavior, and a usage workflow, making it fully complete for a moderately complex tool.

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 the baseline is 3. The description reinforces the purpose of 'text' (fetched record), 'query' (natural-language), and 'limit' (top-N), but does not add format or syntax details beyond the schema. It primarily restates what schema already documents.

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 opens with a precise verb+resource: 'Semantic search INSIDE a fetched record,' clearly distinguishing it from fetch tools. It also provides concrete examples (SEC 10-K body, article) and explicitly pairs with ask_pipeworx_grounded, making its unique role unmistakable.

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?

The description gives an explicit when-to-use instruction: 'Use when the record is too big to cram into the prompt.' It also outlines a workflow with ask_pipeworx_grounded ('fetch with the gateway, ground over the relevant passages instead of the whole document'), which is a clear alternative and integration path.

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

A3.6/5.0
Disambiguation2/5

The set contains several heavily overlapping clusters: three ask_pipeworx variants (with ask_pipeworx_beta explicitly identical to ask_pipeworx right now) and six polymarket-related tools that all orbit edge detection, arbitrage, and fill risk. An agent choosing among bet_research, polymarket_edges, polymarket_arbitrage, and polymarket_kalshi_spread would have a hard time picking the right one.

Naming Consistency3/5

Most tools use readable snake_case, so the naming is not chaotic. However, the pattern is mixed: some are verb_noun (compare_entities, resolve_entity), some are brand-style (ask_pipeworx, ask_pipeworx_beta), and some are noun-only phrases (events, polymarket_arbitrage, pipeworx_trending). It is consistent in casing but not in structural convention.

Tool Count2/5

32 tools is well beyond the usual well-scoped range, and the count is inflated by multiple near-duplicate clusters for querying, prediction markets, and memory/subscription utilities. For a server named Madrid Events, this is especially disproportionate since only one tool actually relates to Madrid events.

Completeness2/5

Relative to the Madrid Events name, the domain coverage is almost entirely missing: only events addresses the stated purpose, and it is read-only with no detail view, booking, or management operations. If interpreted as the broader Pipeworx platform, coverage is richer, but the server's stated identity makes the gap severe.

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