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Walkscore

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

A4.4/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, etc. Description adds rich behavioral details: BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation and flagging, and offset for verification. No contradiction.

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?

Well-structured, brief (~100 words). Front-loaded with the main action, then use case, then technical details. Every sentence adds value with no fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers return format (top-N passages with offsets and similarity scores), cap and truncation, and pairing with sibling. Could mention error handling or what 'flagged' means, but overall complete for a non-output-schema 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% (all 3 parameters have descriptions). The description reinforces the schema with context but adds minimal new parameter-specific information beyond what the schema already provides.

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' with specific examples (SEC 10-K body, article). It distinguishes from sibling tools by explicitly pairing with ask_pipeworx_grounded and explaining when to use this tool over alternatives.

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?

Provides explicit guidance: 'Use when the record is too big to cram into the prompt' and explains pairing with ask_pipeworx_grounded. Does not explicitly list when not to use, but the context is clear and helpful.

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

A4.1/5.0
Disambiguation3/5

Tools are generally distinct but several clusters (ask_pipeworx family, polymarket family) have highly similar names that could cause confusion. For example, ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all appear to do similar things with subtle differences. The agent would need to read descriptions carefully to pick the right one.

Naming Consistency4/5

Most tools follow a verb_noun pattern (e.g., compare_entities, resolve_entity, validate_claim). However, a few are single verbs (remember, recall, forget) or have inconsistent suffixes (ask_pipeworx_beta, walkscore_score). This is mostly consistent but not perfectly uniform.

Tool Count3/5

32 tools is quite high, bordering on excessive. While each tool serves a distinct purpose, the sheer number may overwhelm the agent. However, the tools cover a wide range of functionality, so the count is not unreasonable for a comprehensive data platform.

Completeness4/5

The tool set is comprehensive for querying data, conducting research, managing subscriptions, and evaluating bets. It includes meta-tools for discovery and memory. Notable gaps include user account management and direct file handling, but overall coverage is strong.

Resources