Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Open and Async MCP Server Should we do the design review sync or async? Triage it for me."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Bring async-first working practices into your editor and AI assistant. This Model Context Protocol server gives your AI the tools to draft a decision doc, turn a meeting into an async artifact, pressure-test a status update, or settle a sync-vs-async debate — without leaving the tools you already work in.
It's the method from Open and Async — the collaborative software-development playbook for remote and distributed teams — as working tools, not reading.
Stop writing decision docs from a blank page. Hand your AI the decision and the options; get back a structured ADR scaffold — context, tradeoffs, the call, and how reversible it is.
Turn "let's hop on a call" into an artifact. Give it a meeting's purpose and agenda; get the async equivalent with an owner and a deadline.
Ship status updates that don't blindside anyone. Score a draft against the "work loudly / no surprises" rubric and get concrete fixes before you post it.
Settle sync vs. async in seconds. Describe the task; get a recommendation and the rule behind it.
Run a standup without a meeting. Drop in a structured async-standup template your team can adopt today.
Win the "async is too slow" argument. Map common objections to a ready reframe, pull role-aware guidance, or search the book's principles — each answer cited and linked.
Get coached end to end. Run the coach prompt with a situation and it triages
sync vs. async, generates the right artifact, adds role guidance, and caps with a
shareable line — one composed answer, or a nudge to just have the conversation when
that's the better call.
Related MCP server: Coach AI
Claude Code:
claude mcp add open-async -- npx -y @open-and-async/mcpClaude Desktop (claude_desktop_config.json):
{
"mcpServers": {
"open-async": {
"command": "npx",
"args": ["-y", "@open-and-async/mcp"]
}
}
}Any MCP client that speaks stdio works the same way: run npx @open-and-async/mcp.
Tool | What it does |
| Decision + options → a structured ADR/decision-doc scaffold (context, options, tradeoffs, decision, reversibility). |
| Meeting purpose/agenda → the async equivalent (artifact, owner, deadline). |
| Scores a draft update against the "work loudly / no surprises" rubric and suggests fixes. |
| A structured async-standup template a team can adopt. |
| Recommends sync vs async for a task, with the decision rule. |
Tool | What it does |
| Sections + chapters + one-line TL;DRs. The map. |
| A chapter's TL;DR + taglines + read-the-chapter link. |
| Keyword search over the summary corpus; short cited snippets. |
| Maps skepticism ("async is slow") to the book's reframe. |
| Role-aware ( |
| Taglines + their |
book://outline — sections, chapters, TL;DRs (JSON)
book://taglines — taglines + quote-card URLs (JSON)
book://about — what this server is, what it does and doesn't contain, and where to get the book
Invoke these directly from your client (e.g. as slash commands):
coach — the full method on one situation. Triages sync vs. async, generates
the right artifact, enriches with role guidance, and caps with a shareable tagline —
one composed deliverable. Stops and recommends a real-time conversation when the
situation genuinely needs one.
async-standup, write-adr, meeting-to-issue, weekly-update — parameterized
templates for a single task.
It's a real tool, not a paywall. The method tools work on their own — no book
required. The reference tools answer from the book's already-public summaries and a
paraphrased framework layer, always capped and always cited so you can trace any
snippet to its chapter. The "get the book" link surfaces about once per session, not
on every line — it's a tool, not an ad. No verbatim book prose is bundled (the only
data file is data/book.json.br), so what you install is genuinely useful, not a
teaser.
Staying current. data/book.json.br ships with a version that tracks the book's
edition, so the server can tell you which edition it's based on and re-sync when a new
one lands.
Generated output is a template, not the author speaking. The method tools format your input into decision docs, standups, and triage calls. That output is generated guidance to adapt — it is not a personal statement, quote, or endorsement by Ben Balter or Open & Async LLC, and shouldn't be presented as one. Reference-tool snippets are summaries the tool cites back to the book, not verbatim prose.
This package is split-licensed — see LICENSE for the overview:
Code (everything under src/) — MIT.
Data (data/book.json.br) — proprietary; © Open & Async LLC. You may use it only
as part of running this software. No redistribution as a standalone dataset, no
derivative datasets, no model training. See DATA-LICENSE.md.
The full work — the stories, the voice, the complete argument — lives in the book: open-and-async.com.
The map of Open and Async: every section and chapter with a one-line TL;DR. Use it to find the right chapter for a topic.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool returns a structured outline with TL;DRs, but does not explicitly state whether the operation is read-only or if any side effects exist. The behavior is implied but not fully explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences that front-load the core purpose (map of the book) and immediately state the use case. No fluff or redundant repetition of the tool name.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple no-parameter tool without an output schema, the description is quite complete: it specifies the content (sections/chapters with TL;DRs) and a primary use case (finding the right chapter). Minor gap: it doesn't describe the exact output format (e.g., list vs. nested tree), but this is not critical for such a simple tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters, and the schema coverage is 100% (empty properties). Per the rubric, the baseline for 0 parameters is 4. The description adds no parameter info because none exists, which is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as a map of the book 'Open and Async' containing every section and chapter with a one-line TL;DR. It distinguishes itself from siblings like get_chapter_summary by positioning itself as the overview/map rather than a detailed chapter-specific tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly states when to use it: 'Use it to find the right chapter for a topic.' This provides clear context for use, though it does not mention exclusions or alternatives beyond the implicit distinction from sibling tools like get_chapter_summary.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Given a meeting's purpose or agenda, propose the async equivalent: the artifact that replaces it, where it lives, who decides, and the deadline. Meetings are a point of escalation, not the default.
| Name | Required | Description | Default |
|---|---|---|---|
| agenda | No | Agenda items, if any. | |
| purpose | Yes | Why the meeting exists / what it's meant to accomplish. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses the output structure (artifact, location, decision-maker, deadline) and frames meetings as escalation, which is useful. However, it does not state whether the tool has side effects (e.g., creating a document) or describe the response format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the core function and output components. The second sentence adds philosophical guidance without redundancy. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter tool with no output schema, the description covers the input, output components, and the tool's philosophy. It could explicitly mention return format or side effects, but it is largely complete for typical usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already provides 100% parameter coverage. The description references 'purpose or agenda', reinforcing the schema's semantics, but adds no new syntax or formatting details. This aligns with the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function with a specific verb ('propose') and resource ('async equivalent of a meeting'). It distinguishes from siblings like triage_sync_vs_async by focusing on converting an existing meeting rather than deciding sync vs. async.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the use case: given a meeting's purpose or agenda, generate an async alternative. The phrase 'Meetings are a point of escalation, not the default' provides context on when to use it, but it does not explicitly mention exclusions or alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Turn a decision plus its options into a structured, async-friendly decision record (ADR): context, options with tradeoffs, the decision, and its reversibility. Write the decision down so others can follow it without a meeting.
| Name | Required | Description | Default |
|---|---|---|---|
| context | No | Background: what forces this decision now. | |
| options | No | Candidate options. Two or three is plenty. | |
| decision | Yes | The decision to be made, in one line. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool produces a structured ADR containing context, options with tradeoffs, the decision, and reversibility. However, it does not mention permissions, side effects, or how the draft is stored or returned. For a drafting tool, this is minimally adequate but lacks richer behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the primary verb and target resource. Every phrase contributes meaning, including the outcome ('structured, async-friendly decision record') and the motivation ('so others can follow it without a meeting'). There is no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple three-parameter tool with no output schema, the description is nearly complete: it explains the purpose, the content of the record (context, options, decision, reversibility), and the async context. However, it does not explicitly describe the return value or how the draft is delivered, leaving a small gap in behavioral expectations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema descriptions cover 100% of parameters, establishing a baseline of 3. The description adds semantic value by specifying that options should include 'tradeoffs' and that the record should include 'reversibility,' concepts not present in the schema's parameter descriptions. This elevates it above baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb and resource: 'Turn a decision plus its options into a structured, async-friendly decision record (ADR).' It explicitly names the output type and key components, distinguishing it from sibling tools like convert_meeting_to_async or run_async_standup, which focus on meetings or status updates.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context: use this to draft a decision record for asynchronous collaboration, so others can follow it 'without a meeting.' It implies the tool is the written alternative to a discussion, but it does not explicitly name alternatives or state when not to use it, so it falls just short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return a chapter's TL;DR plus its taglines and a link to read the full chapter. Summary only — no body text.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Chapter slug, e.g. 'impact-over-input' (from book_outline). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description takes on full responsibility for behavioral disclosure. It explicitly lists what is returned (TL;DR, taglines, link) and what is excluded (body text), which is sufficient for a simple read-only tool. It adds useful scope clarity, though it does not explicitly state lack of side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with the action and resource front-loaded. It states the return components and a key limitation without any unnecessary words, making it crisp and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 param, no output schema), the description adequately covers what the tool returns and what it does not return. It does not address error behavior, but for a summary retrieval tool this is a minor gap; the description is otherwise complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides a clear description of the single parameter 'slug' with an example and source ('from book_outline'), achieving 100% coverage. Since the schema already explains the parameter thoroughly, the description does not need to add parameter details, and the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Return') and clearly identifies the resource ('a chapter's TL;DR') and its components. It distinguishes itself from siblings like get_taglines by stating it returns taglines as part of a larger summary package.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'Summary only — no body text' gives implied guidance about when this tool is appropriate, but it does not explicitly name alternatives or state when to prefer this over get_taglines or book_outline. It provides a boundary but no direct comparison.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Role-aware guidance (manager or IC) for a topic, paraphrased from the book's role callouts, with a chapter citation.
| Name | Required | Description | Default |
|---|---|---|---|
| role | No | Audience: 'manager', 'ic', or 'any' (default). | |
| topic | Yes | The topic you want guidance on. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the behavioral transparency burden. It adds useful context by noting the output is 'paraphrased' and includes a 'chapter citation', giving insight into output format and source. However, it does not disclose error handling, permission needs, or return structure in detail, leaving some gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single efficient sentence that front-loads the core value ('Role-aware guidance') and packs in the source, paraphrasing, and citation detail without redundancy. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
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, no output schema), the description is sufficiently complete: it conveys the purpose, the role dimension, the source, and the citation behavior. While it doesn't explicitly describe the full return format, that is implied, and the overall context is solid for an informational tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both role and topic having clear descriptions, including the role enum and default. The tool description adds no extra parameter-level meaning beyond what the schema already provides, so the baseline score of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool provides role-aware guidance for a topic, paraphrased from the book's role callouts, with a chapter citation. It clearly distinguishes this from sibling tools like get_chapter_summary and search_principles by specifying the role-aware nature and the citation output.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when the user needs role-specific guidance from the book, as it explicitly mentions manager or IC roles. However, it does not explicitly state when not to use it or name alternatives, so it stops short of full usage differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return the book's shareable taglines and their quote-card URLs. Optionally filter to one chapter.
| Name | Required | Description | Default |
|---|---|---|---|
| chapter | No | Chapter slug to filter by (omit for all). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears the full burden of disclosing behavior. It states the tool returns taglines and quote-card URLs, implying a read-only operation, but does not mention any side effects, error handling, pagination, or return format details. This is insufficient for a tool without annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of two short sentences, each serving a clear purpose: the first identifies the primary return value, the second notes the optional filter. It is front-loaded with the verb 'Return' and contains no filler or redundant phrases.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one optional parameter and no output schema, the description covers the core functionality (returning taglines and URLs) and the filtering option. It implicitly communicates that omitting the chapter returns all taglines, though it could explicitly state that for full completeness. Overall, it is adequate for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema documents the 'chapter' parameter with a description ('Chapter slug to filter by (omit for all).'), and schema coverage is 100%. The description's 'Optionally filter to one chapter' adds minimal new meaning beyond the schema, so it meets the baseline for fully covered parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Return' and clearly identifies the resource as 'the book's shareable taglines and their quote-card URLs.' This is distinct from sibling tools like get_chapter_summary or book_outline, as it uniquely targets taglines and their associated URLs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by stating what the tool does (returning taglines) and noting an optional chapter filter, but it does not explicitly mention when to use this tool versus alternatives like get_chapter_summary. There are no exclusions or alternative tool references, so guidance is limited to inferred context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Map a common objection to open/async work ('async is slow', 'remote kills culture') to the book's reframe, with a chapter citation.
| Name | Required | Description | Default |
|---|---|---|---|
| objection | Yes | The skepticism or pushback to address. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It clearly describes the tool's behavior: accepts an objection and returns a reframe plus chapter citation. It implies a non-destructive, read-only mapping operation, though it does not explicitly elaborate on return format or limitations beyond 'common objections'.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with a clear verb. Every element earns its place: the action, the scope of objections, examples, and the citation behavior. There is no redundancy or vague filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple single-parameter tool with no output schema and no annotations, the description covers the core purpose, input scope, and output format (reframe + citation). It could mention edge cases (e.g., unrecognized objections) but is otherwise complete for the tool's simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 100% coverage with a clear description, but the tool description adds meaningful semantic context: it narrows the objection to common concerns about async/remote work and provides examples. This goes beyond the schema's generic 'skepticism or pushback' and clarifies expected input.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: mapping a common objection to open/async work to the book's reframe with a chapter citation. It uses a specific verb ('Map') and identifies the resource (objections), distinguishing it from sibling tools that retrieve content or create documents.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool (when a common objection to async/remote work is raised), and gives concrete examples. However, it does not explicitly mention alternatives or when not to use it, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return a structured async-standup prompt a team can adopt in any chat or issue — replaces the daily sync standup with a written, searchable thread that respects every time zone.
| Name | Required | Description | Default |
|---|---|---|---|
| cadence | No | e.g. 'daily', 'twice a week'. Defaults to daily. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It explicitly states the tool returns a prompt and notes the behavioral benefit (replaces sync with written, searchable thread). This is sufficient for a simple, non-mutating template generator, though it could be more explicit about side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that immediately states the action and outcome, with no wasted words. It is front-loaded and highly readable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one optional parameter and no output schema, the description covers the tool's purpose, output type, and practical benefit. No critical information is missing for an agent to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% (the 'cadence' param includes examples and default). The tool description does not add meaning beyond the schema for this parameter, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb+resource pair: 'Return a structured async-standup prompt'. It also clearly distinguishes the tool from siblings by noting it 'replaces the daily sync standup' with a written, searchable thread, which positions it against similar async/meeting tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool: to replace daily sync standups with an async, timezone-friendly thread. It does not explicitly name alternative sibling tools or state when not to use it, but the context is strong enough to infer usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Score a draft status update against the 'work loudly / no surprises' rubric and suggest fixes. Good updates surface blockers early, state outcomes over activity, and link to the work.
| Name | Required | Description | Default |
|---|---|---|---|
| update | Yes | The draft status update to score. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It explains that the tool scores against a rubric and suggests fixes, adding context about its analytical nature. However, it does not explicitly state whether the operation is read-only, what output format is returned, or any side effects. For a simple scoring tool, this is adequate but not thorough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no filler. The first sentence front-loads the action and context; the second sentence explains the rubric criteria. Every phrase earns its place, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool without an output schema, the description covers the core purpose and scoring criteria. It implies the output (score and suggested fixes) but does not explicitly describe the return format. Given the low complexity, this is nearly complete, though a bit more detail on output would make it fully self-contained.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single parameter 'update', which is described as 'The draft status update to score.' The description adds no additional meaning beyond this, just repeating 'draft status update.' Since the schema already provides full coverage, the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb 'Score' and resource 'draft status update' against a named rubric, and it lists rubric criteria ('surface blockers early, state outcomes over activity, link to the work'). This clearly distinguishes it from sibling tools like run_async_standup or convert_meeting_to_async, which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description makes clear that the tool is for scoring a draft status update, providing context for when to use it. It does not explicitly mention alternatives or exclusions, but the purpose is unambiguous and the sibling tools are unrelated, so the implied usage is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Keyword search across the book's summary corpus (TL;DRs, key-takeaways, taglines, and reviewed frameworks). Returns short, cited snippets — a snippet view, not full text.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 5, capped at 10). | |
| query | Yes | What you're looking for. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the tool returns short, cited snippets—not full text—and specifies the search scope. This gives important behavioral context about output format, but it does not mention potential behaviors like result ordering or error handling. For a simple search tool, this is reasonably transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of two tightly focused sentences. The first sentence states the action and scope, the second describes the output format. Every word contributes, there is no filler, and the key behavioral note ('not full text') appears clearly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 parameters, no output schema), the description provides sufficient context: it names the corpus being searched, the types of content included, and the return format. It covers the essential concerns an agent would have when deciding whether to invoke this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The description adds context like 'search across the summary corpus' and 'snippet view,' but it does not elaborate on query syntax or limit semantics beyond what the schema already states. The value added over the schema is minimal.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Keyword search') and identifies the exact resource ('the book's summary corpus'), listing the content types included (TL;DRs, key-takeaways, taglines, reviewed frameworks). It clearly distinguishes itself from retrieval tools like get_taglines or get_chapter_summary by framing it as a cross-cutting search rather than a single-item fetch.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use this tool: when you need to find relevant snippets across multiple summary types, not full text. It also notes the output is 'a snippet view, not full text,' which signals a lightweight lookup. However, it does not explicitly name alternatives or provide 'when-not-to-use' guidance, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recommend whether a task should be handled synchronously or asynchronously, using the decision rule from 'meetings are a point of escalation.' Async is the default; sync is the escalation.
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | The task, conversation, or decision to triage. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses the core decision rule and the default/escalation behavior. The verb 'Recommend' implies a non-mutating operation, which is helpful context. It does not detail return format or edge cases, but for a simple triage tool, this is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, with the first sentence stating the primary purpose and the second providing the decision rule and default. There is no wasted wording, and the structure is front-loaded with the key information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a single required parameter, no output schema, and no annotations, the description provides sufficient context: it defines the tool's role, the decision rule, and the default behavior. It could mention the output format, but this is not essential for a recommendation triage tool. The reference to a known principle adds meaningful context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single 'task' parameter, and the schema description already explains what it expects. The tool description does not add parameter-specific details beyond the schema, but since the schema fully covers the parameter, the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb and resource: 'Recommend whether a task should be handled synchronously or asynchronously.' It also references a specific decision rule, distinguishing it from sibling tools like 'convert_meeting_to_async' which actually performs a conversion rather than making a recommendation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool: it is for triaging tasks based on the rule that 'meetings are a point of escalation.' It states the default (async) and the escalation (sync), giving practical guidance. However, it does not explicitly mention alternatives or exclusions, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.2book_outlineconvert_meeting_to_asyncdraft_decision_docget_chapter_summaryget_guidanceget_taglineshandle_objectionrun_async_standupscore_status_updatesearch_principlestriage_sync_vs_asyncScored across 11 tools
Each tool has a clear, distinct purpose: from drafting decisions to converting meetings, scoring updates, running standups, triaging sync/async, and accessing book content. Even related tools like convert_meeting_to_async and triage_sync_vs_async differ in scope (specific meeting vs. general task), and the book-access tools each serve unique functions (outline, summary, search, objection mapping, role guidance, taglines).
Almost all tools use snake_case with a verb_noun pattern (draft_decision_doc, convert_meeting_to_async, score_status_update, run_async_standup, get_chapter_summary, etc.). The only outlier is 'book_outline', which is a noun_noun phrase rather than verb_noun, creating a minor inconsistency.
With 11 tools, the server is well-scoped for its purpose of promoting async work practices and providing access to the book's content. It neither feels overloaded nor sparse, fitting comfortably within the ideal 3-15 range.
The tool set covers the core workflows: converting meetings to async, creating decision docs, improving status updates, running async standups, and triaging sync vs async. It also offers comprehensive access to the book's knowledge via outlines, summaries, search, objections, role guidance, and taglines. No obvious missing operations create dead ends for users.
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