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launches

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?

Beyond annotations (read-only, idempotent), the description discloses truncation ('cap is 200K chars (longer inputs are truncated and flagged)'), technical details (BGE-base-en embeddings, cosine, 500-char windows), and output characteristics (offsets and similarity scores). This adds substantial behavioral context 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose and then layered with use case, pairing, and technical details. It is slightly dense but every sentence contributes value; no filler. Slightly longer than necessary but still well-structured.

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 specifies that results are top-N passages with character offsets and similarity scores. It also covers edge cases (truncation flag) and gives a concrete workflow with a sibling tool, making it complete for an agent to understand inputs, outputs, and limits.

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% and each parameter has a thorough description (e.g., 'max ~200K chars', '1-20, default 5', and query examples). The tool description adds contextual examples ('SEC 10-K body') but doesn't materially improve parameter-level semantics beyond the schema, so baseline 3 applies.

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 uses a specific verb ('semantic search') with a clear resource ('inside a fetched record') and concrete examples. It also distinguishes itself from sibling tools by explicitly pairing with ask_pipeworx_grounded and positioning itself as the retrieval step after fetching.

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?

It explicitly states when to use: 'Use when the record is too big to cram into the prompt' and explains the benefit ('saves context, returns only the passages that matter'). It also names a complementary tool (ask_pipeworx_grounded) for grounding over passages, clarifying the workflow.

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 significantly overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all perform data retrieval, with beta currently identical to stable. The polymarket_* family and bet_research also blur boundaries, and discover_tools vs suggest_questions both handle discovery. Despite detailed descriptions, agents are likely to misselect among these overlapping options.

Naming Consistency3/5

All tools use snake_case, but conventions are mixed: some start with verbs (get_launch, search_launches, ask_pipeworx), others are noun phrases (entity_profile, pipeworx_trending), and there are versioned suffixes (ask_pipeworx_beta). While each domain group has internal consistency, the overall set lacks a unified pattern.

Tool Count2/5

35 tools is excessive for a server named 'launches'—only 4 tools (get_launch, get_past_launches, get_upcoming_launches, search_launches) actually relate to space launches. The remaining 31 tools cover unrelated domains like prediction markets, company profiles, and memory, making the count inappropriate and diluting the server's focus.

Completeness2/5

For the claimed launch domain, the set provides only basic list/detail/search operations and lacks useful launch features like filtering by agency, date range, or launch site. More critically, the inclusion of 31 unrelated tools creates a fragmented surface with obvious dead ends—an agent expecting a launch-focused server would find most tools irrelevant.

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