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Htmlentities

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?

Beyond the annotations (readOnlyHint, idempotentHint, etc.), the description discloses the underlying algorithm (BGE-base-en embeddings, cosine similarity), the chunking strategy (500-char overlapping windows), the 200K char cap with truncation flag, and the return structure (offsets and scores). This rich behavioral detail helps the agent predict outcomes without invoking the tool.

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?

The description is five sentences, each with distinct value: core action, use case, companion tool, mechanics, and constraints. It is front-loaded with the primary purpose and maintains density without fluff, earning every sentence.

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 explains the return format (top-N passages, character offsets, similarity scores) and operational constraints (embedding model, window size, character cap). It provides enough context for an agent to select and invoke the tool correctly, including how to verify quotes and integrate with ask_pipeworx_grounded.

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 the baseline is 3. The description adds context like 'text you already pulled' and 'top-N passages' but does not provide new syntax or format details beyond what the schema already documents. It reinforces but does not materially extend parameter understanding.

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 states 'Semantic search INSIDE a fetched record' with a specific verb and resource, clearly distinguishing it from sibling tools that fetch or retrieve data. It emphasizes searching within already-retrieved text and specifies return values (top-N passages, offsets, scores), making the purpose unmistakable.

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?

The description explicitly says 'Use when the record is too big to cram into the prompt' and pairs with ask_pipeworx_grounded, naming an alternative workflow. This gives clear when-to-use and complementary tool guidance, going beyond mere implication.

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

All Htmlentities toolsInspect
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TDQS

A3.9/5.0
Disambiguation2/5

Multiple tools overlap heavily: ask_pipeworx, ask_pipeworx_beta (currently identical), and ask_pipeworx_grounded share the same routing core, while bet_research, polymarket_edges, polymarket_arbitrage, and polymarket_fill_risk all target prediction-market edge detection. The descriptions are detailed, but an agent must read a lot of nuance to avoid selecting the wrong tool.

Naming Consistency3/5

All names are lowercase snake_case and readable, but the convention is mixed: verb_noun names like encode_html and resolve_entity sit alongside bare verbs like remember and forget, and noun-phrase names like entity_profile, recent_changes, and polymarket_fill_risk. The ask_pipeworx_* and polymarket_* families are internally consistent, but there is no single predictable pattern across the whole set.

Tool Count2/5

At 33 tools this exceeds the 25+ threshold for a heavy set. The bloat is especially noticeable because the server is named Htmlentities yet only encode_html and decode_html relate to that purpose; the rest are unrelated Pipeworx research, prediction-market, memory, and subscription utilities.

Completeness4/5

The Pipeworx surface is broadly complete: ask/grounded/deep_research/discover/suggest cover data access, entity_profile/compare_entities/recent_changes/validate_claim cover entity workflows, and subscriptions and memory have create/list/delete lifecycles. Minor gaps such as no update operation for subscriptions or memories are workable, and encode/decode fully covers the literal Htmlentities purpose.

Resources