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words

by io.github.pipeworx-io

Autocomplete

autocomplete
Read-onlyIdempotent

Return word completions for a given prefix string using the Datamuse suggestion endpoint. Takes a prefix (e.g., "hel") and returns matching words ranked by score (e.g., "hello", "help", "helpless").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of results to return (default: 10)
prefixYesThe prefix to autocomplete (e.g. "hel" returns "hello", "help", etc.)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
prefixYesThe prefix that was autocompleted
resultsYesList of word completions ranked by score

TDQS

A4/5.0
Behavior4/5

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

The description adds context beyond annotations by naming the Datamuse suggestion endpoint and explaining that results are 'ranked by score'. While annotations already declare readOnlyHint and idempotentHint, the description enriches understanding of the data source and output ordering without repeating annotation details.

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 exactly two sentences, with the verb and resource front-loaded. It includes a concrete example, and every sentence adds value with no fluff. Perfectly concise and well-structured.

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?

Given the tool's simplicity (2 params, output schema present, strong annotations), the description adequately covers what the tool does and how it behaves. It could mention overlap with sibling tools, but the description is sufficient for a straightforward autocomplete operation.

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 schema already documents both prefix and limit parameters with descriptions and examples. The tool description does not add significant extra meaning beyond the schema's own examples, hence baseline score of 3 is appropriate.

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 states the tool's function: 'Return word completions for a given prefix string' using the Datamuse suggestion endpoint. It provides specific examples ('hel' returns 'hello', 'help') that illustrate the behavior and distinguish it from sibling tools like find_synonyms or find_rhymes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage by showing a prefix example, but it does not explicitly state when to prefer this tool over alternatives such as find_words or explore for related terms. There is no mention of when not to use it, leaving room for ambiguity.

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

A3.6/5.0
Disambiguation3/5

Most tools have distinct purposes, but there is overlap between query tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research, and between prediction market tools like bet_research and polymarket_edges. Descriptions help differentiate, but the boundaries are not always clear.

Naming Consistency2/5

Tool names are all snake_case but lack a consistent pattern. Some start with verbs (ask, compare, find), others are nouns (autocomplete, entity_profile), and many are long phrases (ask_pipeworx_grounded, scan_competitor_ai_presence). The naming feels ad-hoc and not easy to predict.

Tool Count2/5

At 35 tools, this server is over-packed for a server named 'words'. Many tools are unrelated to words (e.g., prediction markets, subscriptions, entity profiles). The scope is too broad, making it feel like a catch-all rather than a coherent set.

Completeness3/5

The word tools are limited (only 6), leaving obvious gaps for a word-focused server (e.g., no dictionary lookup, no word definitions). However, the server covers a wide range of data domains through meta-tools like ask_pipeworx, which compensates but makes the purpose unclear.

Resources