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Jisho

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?

The description goes beyond the annotations (readOnlyHint, idempotentHint) by disclosing technical details: BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, a 200K char cap with truncation and flagging. These details help the agent understand constraints and behavior, adding significant value.

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 a single paragraph that is concise and front-loaded with the primary purpose. It packs substantial information without being verbose. Minor restructuring could improve readability, but it remains efficient.

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?

Despite lacking an output schema, the description clearly states the output format: 'top-N passages with character offsets and similarity scores'. It also covers input constraints, use cases, and technical details. For a tool with three simple parameters, the description is comprehensive and leaves no major gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All three parameters are described in the schema with 100% coverage. The description adds context to the 'text' parameter (e.g., 'max ~200K chars') and provides natural-language query examples for 'query', enhancing understanding beyond the schema descriptions. The default for 'limit' is already in 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?

The description clearly identifies the tool as performing semantic search inside a fetched record. It specifies the action ('search'), the resource ('a fetched record'), and the output ('passages with character offsets and similarity scores'). It distinguishes from sibling tools like 'search_words' by emphasizing internal search and pairs with 'ask_pipeworx_grounded', showing clear differentiation.

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?

The description explicitly states when to use the tool: 'Use when the record is too big to cram into the prompt'. It also provides a complementary tool ('ask_pipeworx_grounded') for alternative use. While it does not list specific cases for avoidance, the positive guidance is strong and clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Jisho toolsInspect
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TDQS

A3.8/5.0
Disambiguation3/5

Most tools have distinct purposes, but there are overlapping tools like the three ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and ai_visibility_check/scan_competitor_ai_presence. A few pairs could cause confusion, but overall differentiation is moderate.

Naming Consistency2/5

Tool names follow no consistent pattern: some are imperative verbs (remember, forget), some are compound nouns (entity_profile, polymarket_arbitrage), some are descriptive phrases (recent_alerts, scan_dependency). The verb_noun pattern is absent, leading to inconsistency.

Tool Count1/5

33 tools is far too many for a server named 'Jisho' (a Japanese dictionary), especially since only 2 tools (lookup, search_words) are dictionary-related. The majority of tools belong to an unrelated data platform, making the count wildly inappropriate for the server's apparent purpose.

Completeness3/5

For the dictionary domain, the server lacks common features like example sentences or kanji details. However, the extended tool set covers many data retrieval and analysis tasks, though it is read-heavy with no update/delete capabilities for most resources.

Resources