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Could Have Been Email

by io.github.pipeworx-io

Could Have Been Email Analyze

could_have_been_email_analyze
Read-onlyIdempotent

Check if a meeting transcript could have been an email instead. Returns filler word count, decisions made, action items, and a suggested email that would've replaced the meeting.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
durationNoMeeting duration in minutes
recurringNoIs this a recurring meeting
transcriptYesMeeting transcript or summary
attendee_countNoNumber of attendees

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
action_itemsNoList of action items from the meeting
decisions_madeNoList of decisions made during the meeting
suggested_emailNoEmail text that would have replaced the meeting
filler_word_countNoCount of filler words in the transcript
could_have_been_emailNoWhether the meeting could have been an email

TDQS

A3.9/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, etc. The description adds what the tool returns, which is partially redundant with the output schema. No additional behavioral traits (e.g., rate limits, data handling) are disclosed.

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 a single sentence with a clear verb and resource, followed by a brief list of outputs. No filler, well-structured, and all information is essential.

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?

The tool is simple with 4 parameters and an output schema. The description fully explains what the tool does and what it returns. No additional info is needed for an agent to use it correctly.

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% with all parameters described. The description does not add new semantic meaning beyond the schema. It naturally explains the transcript parameter but gives no extra detail on duration, recurring, or attendee_count.

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 purpose: 'Check if a meeting transcript could have been an email instead.' It lists specific outputs (filler word count, decisions, action items, suggested email) and the tool's name and title reinforce this. No ambiguity.

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?

Usage is implied by the tool's specific function, but there is no explicit guidance on when to use this tool versus alternatives or when not to use it. The description could mention that it is for transcripts only or that it is not for real-time meetings.

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

All Could Have Been Email toolsInspect
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TDQS

A3.9/5.0
Disambiguation2/5

Several tool clusters are nearly indistinguishable in purpose: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route to the same underlying catalog, and the six polymarket tools heavily overlap in surfacing prediction-market edge. Even with detailed descriptions, an agent could easily misselect between bet_research and polymarket_edges or between discover_tools and suggest_questions.

Naming Consistency3/5

Most names use lowercase snake_case, but the pattern is mixed: some are verb_noun (compare_entities, resolve_entity), some are bare verbs (remember, forget, recall), and some are compound noun phrases (polymarket_edges, pipeworx_trending). ask_pipeworx also breaks the separator convention compared to ask_pipeworx_beta and ask_pipeworx_grounded.

Tool Count2/5

With 32 tools, this exceeds the 25+ threshold for 'too many' and feels like a platform bundle rather than a focused server. It spans data querying, prediction markets, memory, subscriptions, feedback, AI visibility, dependency scanning, and llms.txt generation, which is far more surface area than one coherent server should present.

Completeness4/5

For the core data-research and prediction-market domains, coverage is strong: query, grounded verification, deep research, entity resolution, comparisons, change feeds, arbitrage, fill-risk, subscriptions, and memory are all present with no major dead ends. The gaps are mostly the single-purpose oddballs (could_have_been_email_analyze, generate_llms_txt, scan_dependency) that don't connect to the rest of the surface.

Resources