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NYC Open Data

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

A4.6/5.0
Behavior5/5

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

Discloses beyond annotations: uses BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation flag, returns offsets. Annotations only show readOnlyHint and idempotentHint; description adds critical behavior.

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?

Compact, front-loaded with purpose and key benefit. Each sentence adds value; 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 usage, behavior, and pairing advice. No output schema exists, but description mentions return content (passages, offsets, scores). Missing explicit statement of 'top-N passages' but implied.

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 already describes all 3 parameters (100% coverage). Description adds query examples and char limit context but doesn't significantly extend meaning.

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?

Describes specific operation: semantic search inside a fetched record. Differentiates from sibling tools like ask_pipeworx_grounded by explicitly stating it works over retrieved text, not a database.

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 states when to use: when a record is too large for the prompt. Names alternative: ask_pipeworx_grounded for grounded answers 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

A3.7/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical routers, and the five polymarket_* tools all perform prediction-market analysis. Even with detailed descriptions, an agent could easily select the wrong one, especially since the server name 'NYC Open Data' does not hint at this focus.

Naming Consistency4/5

Most tools follow a consistent lowercase snake_case verb_noun pattern (e.g., list_subscriptions, generate_llms_txt, resolve_entity) and families share prefixes like polymarket_ and pipeworx_. Minor deviations exist with one-word names like datasets, metadata, and forget, but overall the naming is readable and predictable.

Tool Count2/5

34 tools is far too many for a server labeled 'NYC Open Data,' where only 3 tools (datasets, metadata, query) actually serve that purpose. The rest form a general-purpose data platform, but even then the count is high and includes many redundant meta-tools and overlapping Polymarket utilities, making the set feel bloated.

Completeness3/5

For the NYC Open Data domain, the three dedicated tools cover search, metadata, and query—adequate core functionality but missing export or dataset management capabilities. For the broader Pipeworx platform, coverage is strong (routing, grounded answers, deep research, entity profiles, subscriptions, memory), but the severe mismatch between the server name and actual scope leaves a major completeness gap.

Resources