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Endoflife

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?

Annotations already cover safety profile. Description adds: embedding model (BGE-base-en), chunking (500-char windows), similarity (cosine), char cap (200K, truncation flagged), offset per passage. 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?

Single paragraph, front-loaded with purpose, every sentence adds value (use case, mechanics, limitations). No waste.

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?

No output schema, yet description covers return format (passages, offsets, scores). Explains internal process and constraints. Complete for agent decision-making.

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 baseline 3. Description reinforces schema details (200K char cap) and provides example queries but doesn't add significant new parameter semantics beyond the schema.

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?

Clearly states it performs semantic search inside a fetched record. Uses specific verb 'search inside' and resource 'fetched record'. Distinguishes from sibling 'ask_pipeworx_grounded' by explaining how they pair.

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 says when to use: 'when the record is too big to cram into the prompt'. Mentions alternative 'ask_pipeworx_grounded' and explains benefits: saves context, returns only relevant passages with offsets.

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

The tool set mixes three near-identical ask_pipeworx variants, multiple overlapping prediction-market tools (polymarket_edges, polymarket_arbitrage, bet_research), and three endoflife tools buried among 31 unrelated Pipeworx tools. This makes distinguishing between tools genuinely confusing, especially when several appear to route to the same underlying data.

Naming Consistency3/5

Most names use lowercase snake_case, but the verb-noun pattern is inconsistent: some are verb_noun (list_products, get_product), others noun_noun (polymarket_edges, bet_research), and a few are bare verbs (recall, forget). The style is readable but does not follow a single predictable convention.

Tool Count2/5

With 34 tools, the count is far too high for a server named 'Endoflife'—only three tools actually relate to endoflife.date tracking. The remaining 31 tools belong to a separate Pipeworx platform, making the tool count an extreme over-scoping for the apparent purpose.

Completeness4/5

The endoflife subset is complete: list_products, get_product, and get_cycle cover the full lifecycle of discovering and retrieving release/support timelines with no dead ends. The broader Pipeworx toolkit also appears fairly comprehensive for its own domain, but the mixed set makes it hard to assess a single coherent surface.

Resources