DevSpeak is a writing tool for developers, available to your coding agent over MCP. Call optimize_text to improve a PR description, commit message, ticket or doc in the language it was written in, or set correctionsOnly to fix only spelling, grammar and punctuation. Call translate_text to turn rough notes into a technical spec, Jira tickets, an RFC, an API design or a data model for the audience you choose (Junior Dev, Senior Dev, Tech Lead, SRE or Data Engineer). It also translates README files with translate_markdown, refines earlier output and lists your history. Requires a DevSpeak API key; a free account includes one key with 50 API requests a month.
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Scored across 12 tools
Most tools target distinct operations, but optimize_text substantially overlaps with translate_text's 'Optimize' format (both improve text in its own language), and refine_translation also sits in the same improvement pipeline. The descriptions attempt to differentiate them via 'Stage 1' language, but an agent could easily misselect among the three.
All 12 tools follow a consistent verb_noun snake_case pattern (get_account_info, translate_text, patch_translation_note, list_languages). No mixing of conventions, and names are predictable and readable.
Twelve tools is well within the ideal range and each maps to a plausible distinct capability (auth, language discovery, translation variants, refinement, summarization, history, publishing). Nothing feels padded or missing at the count level.
The translation surface is fairly complete (translate text/markdown, optimize, refine, detect/summarize, history), but the 'DevSpeak Notes Workspace' is only reachable via patch_translation_note — no create, read, list, or delete note operations exist, leaving a notable gap. publish_linkedin_post is also gated behind admin privileges, so standard-key agents hit a dead end.
Verifies the current API credentials against the DevSpeak backend.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. 'Verifies' implies a read-only, non-mutating check with no side effects, which is useful, but it says nothing about what a failed verification looks like, whether it errors or returns a negative result, or any auth/rate-limit constraints.
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?
A single front-loaded sentence with no filler; the operation and its object come first. Nothing could be trimmed without losing meaning.
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 zero-param tool with no output schema, the description is adequate on what the call does but silent on the return shape (boolean? error? account details?), which is exactly the information an agent needs to interpret the result. No annotation coverage exists to fill that gap.
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 tool takes zero parameters, so the schema requires no parameter explanation and the baseline is 4. The description correctly implies the credentials are taken from the ambient session rather than passed as arguments.
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?
States a specific verb ('Verifies') and resource ('current API credentials') plus the target system ('DevSpeak backend'), so the agent knows exactly what the call does. It does not name any sibling, but the auth-domain purpose is self-evidently distinct from the translation-oriented siblings.
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?
Usage is only implied: an agent can infer it should call this to check whether its credentials are still valid before other work. There is no explicit when-to-use, when-not-to-use, or ordering guidance relative to siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Detects the primary natural language of the input text.
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | Text to analyze |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden and falls short: it says it detects the 'primary' language, implying a single dominant result, but does not disclose the output form (ISO code vs. name), confidence handling, or behavior on mixed/ambiguous input. This is a meaningful gap for a tool with zero annotation coverage.
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?
A single front-loaded sentence with no wasted words. It is efficient, though arguably terse for a tool that needs to convey more about its output.
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?
There is no output schema and no annotations, so the description must carry extra weight, yet it omits what the caller receives – a language code, a name, a confidence score – which is critical for a detection tool. Given the sibling list_languages suggests a controlled vocabulary, the missing return-value context is a real gap.
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%, so the single parameter ('input': 'Text to analyze') is already documented. The description adds no syntax, format, or constraint detail beyond the schema, so 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?
States a specific verb ('Detects') and resource ('the primary natural language of the input text'), so the agent knows exactly what the tool does. It does not, however, distinguish itself from siblings like list_languages or translate_text, which could reasonably be confused with language identification.
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?
There is no guidance on when to use this tool versus alternatives such as list_languages or translate_text, nor any prerequisite notes. The agent must infer from the name alone that this is a detection utility rather than a lookup or translation tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns the account's tier, enabled features, and quotas so an agent can check entitlement before calling a gated tool.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden. It discloses the read-only nature implicitly ('Returns') and enumerates the returned fields, which is useful. It says nothing about authentication requirements, whether the result is cached or user-scoped, or what happens on an unauthenticated call, leaving meaningful gaps for a no-annotation tool.
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?
One sentence, front-loaded with what is returned and followed by the reason to call it. No filler, no restatement 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?
With no output schema, the description is the only place that reveals the return shape, and it does so by listing tier, features, and quotas. For a zero-parameter read tool that is nearly sufficient; only error or empty-entitlement behavior is unaddressed.
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 declares zero parameters, so the 4 baseline applies; there is no parameter syntax the description could usefully add. Nothing in the description conflicts with the empty parameter object.
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 gives a specific verb ('Returns') and names the exact resource contents: the account's tier, enabled features, and quotas. That is concrete enough that an agent can distinguish this from every sibling tool (translate_text, summarize_input, publish_linkedin_post, etc.), none of which report entitlement data.
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 supplies a clear use condition: check entitlement before calling a gated tool. That is real guidance rather than an implied purpose. It stops short of naming which sibling tools are gated or what an agent should do when a feature is missing, so it is not a full when/when-not/alternatives statement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Lists the user's previous translations so prior work can be reused instead of regenerated.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max translations to return (default 50) | |
| offset | No | Pagination offset |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full disclosure burden. It usefully establishes that the data is the caller's own prior translations and that the operation is a non-mutating listing, but says nothing about ordering, recency, pagination behavior, or the shape of a returned entry.
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?
A single front-loaded sentence that names the action, the resource, and the motivation with zero filler. Nothing is repeated and nothing needs trimming.
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 read tool with 100% schema coverage this is close to adequate, but with no annotations and no output schema the description leaves the return value ('previous translations') undefined, so an agent cannot tell what it will actually get back or how to reuse it.
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?
Both parameters (limit, offset) are fully documented in the schema, including defaults and bounds, so the schema does the heavy lifting. The description adds no parameter-level detail such as ordering semantics or whether offset paging is stable, so the baseline 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?
States a specific verb ('Lists') and resource ('the user's previous translations'), making the operation immediately clear. It does not differentiate itself from any sibling, but no sibling in the list (translate_text, refine_translation, etc.) overlaps with a history/listing operation, so confusion risk is low.
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 clause 'so prior work can be reused instead of regenerated' implies why an agent would call it (look up an existing translation before re-translating), but it never states when to use this versus re-running translate_text or refine_translation, and gives no exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns a list of supported natural languages.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations and no output schema, the description carries the burden, but for a zero-parameter read-only enumeration the safety profile is inherently trivial. It does not disclose the return format (language codes vs display names), which is the one piece of behavior an agent would need.
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?
A single front-loaded sentence with no filler. Appropriately sized for a zero-argument tool.
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 trivial enumeration tool with no parameters, no annotations, and no output schema, the description is nearly complete. The only minor gap is the shape of the returned list, which matters little for a tool this simple.
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 input parameters, so the baseline of 4 applies. No parameter meaning needs explaining, and the description correctly makes no claim about inputs.
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?
States a specific verb (Returns) and resource (list of supported natural languages), which an agent can distinguish from the sibling detect_language or translate_text. It doesn't explicitly name a sibling to contrast with, so it stops short of a 5.
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?
No when-to-use context, prerequisites, or named alternatives. An agent can infer this is an enumeration helper, but the description gives no explicit guidance on when to prefer it over detect_language or the translate tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Stage 1 — improves raw text in its own language (grammar, word choice, flow, clarity) without changing meaning or translating it; correctionsOnly limits it to spelling, grammar and punctuation. Use before translate_text when the input is rough.
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | Raw text to polish | |
| context | Yes | Tech context (tone calibration only) | |
| audience | Yes | Target audience (tone calibration only) | |
| managedModel | No | Pin a specific managed model (server-side allowlist) | |
| correctionsOnly | No | Write mode: true fixes spelling, grammar and punctuation only; omitted or false improves the whole text (word choice, flow, clarity). Applies to optimize_text and to format "Optimize". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the full behavioral burden. It does disclose the key semantic constraint (meaning preserved, no translation) and the correctionsOnly write mode, but says nothing about permissions/auth, cost or latency for what is evidently an LLM-backed operation, or whether results are returned inline versus staged for a later step.
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 stage label and core purpose, then the routing hint. Dense but every clause carries information; the semicolon clause is the only slightly compressed part.
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 5-parameter tool with no annotations and no output schema, the definition is only partially complete: it never states what the call returns or how the optimized text is delivered (returned vs. staged for the next stage). The 'Stage 1' hint implies staging but leaves the agent to infer it.
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%, so the schema already documents all five parameters including correctionsOnly, audience and context. The description restates the correctionsOnly semantics rather than adding new syntax or constraints, so it sits at the baseline for fully-covered schemas.
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 gives a specific verb and resource ('improves raw text') plus a precise scope boundary: it operates in the text's own language and explicitly does not translate. Combined with the 'Stage 1' framing, an agent can separate it from translate_text and translate_markdown without opening a schema.
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 states a clear when-to-use condition and pipeline position: 'Use before translate_text when the input is rough.' That is actionable sequencing guidance, but it offers no exclusions (e.g., when to skip optimization, or how it relates to the sibling refine_translation).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Surgically updates a note in the DevSpeak Notes Workspace by applying replace operations.
| Name | Required | Description | Default |
|---|---|---|---|
| ops | Yes | List of patch operations to apply | |
| noteId | Yes | The ID of the note to update |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It says 'updates' but does not state whether operations are atomic, what permissions are required, how partial failures are handled, or what happens to content not touched by the replace operations.
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?
A single front-loaded sentence with no wasted structure. The adverb 'surgically' is mild filler, but the description is otherwise tight and appropriately sized.
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 mutation tool with nested patch operations, no annotations, and no output schema, the description is too thin. It omits how operations are applied, whether they are ordered or all-or-nothing, and what the result of a successful patch looks like.
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%, so the schema already explains noteId and the ops structure. The description adds only the phrase 'replace operations,' which does not provide meaning beyond the schema's detailed op, path, range, and value fields.
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?
States a specific verb (updates) and resource (note in DevSpeak Notes Workspace) plus the mechanism (applying replace operations). It does not differentiate from any sibling note tool, but no such sibling exists in the provided list, so the description is adequately clear.
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?
No explicit when-to-use guidance, prerequisites, or alternatives are given. The description implies that it is for updating a note via replace operations, but an agent gets no help deciding between this tool and other possible note-editing approaches.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Composes a LinkedIn post from a published DevSpeak article. Requires DevSpeak administrator privileges — a standard API key receives 403. Defaults to a preview; pass dryRun:false to publish, which is irreversible.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The article slug, e.g. why-technical-specs-fail | |
| dryRun | No | Compose and return the post without publishing. Default true — always preview before publishing | |
| locale | No | Post language (default 'en') | |
| category | No | Tutorial category. Required when contentType is 'tutorial' | |
| guidance | No | Optional steer on the angle or emphasis | |
| postText | No | Replaces the generated copy. Only honoured when dryRun is false | |
| contentType | Yes | Which published content collection the article belongs to |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does well: it discloses the auth requirement and the 403 failure mode, the preview-by-default behavior, and that publishing is irreversible. It stops short of saying where the post lands, whether it can be edited or deleted afterwards, or any rate limits.
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?
Three sentences, zero waste, correctly ordered: what it does, who may call it, then the preview/publish switch with its irreversibility warning. The destructive detail is front-loaded where an agent will see it before acting.
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 an irreversible tool with no annotations and no output schema, the description covers the critical gaps: permissions, default safety posture, and irreversibility. It omits what a preview actually returns and which LinkedIn identity receives the post, which would matter when the agent reports results back.
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%, so every parameter is already documented in the schema, including the dryRun default and the postText constraint. The description reinforces the dryRun default but adds no new syntax or format detail for slug, contentType, locale, or category, so baseline 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?
States a specific verb and resource: composing a LinkedIn post from a published DevSpeak article. This is unmistakably distinct from the language/translation siblings (translate_text, summarize_input, refine_translation), so an agent can route correctly without opening the schema.
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?
Gives the precondition for use (DevSpeak administrator privileges; standard API key gets 403) and the safe-invocation path (defaults to preview, pass dryRun:false to publish). It does not name or compare against any sibling alternative, but for a single-purpose publishing tool the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Iteratively refines generated output based on user feedback.
| Name | Required | Description | Default |
|---|---|---|---|
| tone | Yes | Tone (0-100) | |
| format | Yes | Output format | |
| context | Yes | Tech context | |
| audience | Yes | Target audience | |
| feedback | Yes | User feedback / changes requested | |
| webSearch | No | Ground generation with real-time web search | |
| managedModel | No | Pin a specific managed model (server-side allowlist) | |
| originalInput | No | Original user input | |
| previousOutput | Yes | Previous generated output | |
| responseFormat | No | Output structure: prose Markdown, or machine-readable XML | |
| contextProjectId | No | Ground generation on a Context Project's instructions/assets | |
| preserveMarkdown | No | Preserve Markdown structure |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full behavioral burden. It mentions iteration and feedback, but says nothing about side effects, persistence, authentication needs, output format, or what happens to previous output.
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 single sentence is front-loaded and free of obvious filler. However, for a 12-parameter tool with no annotations or output schema, the extreme brevity leaves critical information unstated.
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 12 parameters, 6 required fields, no annotations, and no output schema, the description is not complete enough. It omits when-to-use guidance, behavioral details, and return expectations that an agent would need to invoke the tool confidently.
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%, so the schema already documents all parameters. The description adds no additional parameter semantics beyond the input schema, making the baseline score of 3 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?
States a specific verb ('refines') and action basis ('based on user feedback'), but the resource is only 'generated output', not specifically translation. It does not differentiate from sibling tools like optimize_text, translate_text, or patch_translation_note.
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?
No guidance on when to use this tool versus alternatives, nor any prerequisites or exclusions. The description merely implies it is for iterative refinement after receiving feedback.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Condenses text that exceeds the 10,000-character translation limit so it can be translated without truncation.
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | Oversized text to condense below the 10,000-char limit |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations present, the description carries the full disclosure burden. It does disclose the motive and the constraint boundary (10,000-char translation ceiling) and the 15,000 maxLength in the schema implies an accepted input band, but it says nothing about lossiness, whether meaning is preserved, determinism, latency/cost, or whether the output replaces the original.
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?
One sentence, no filler, with the purpose (condense) front-loaded and the reason (avoid truncation) immediately following. Nothing to trim.
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 single-string transform tool this covers the why and the size constraint, but there is no output schema, so the description should say what comes back (the condensed text? a summary object?) and whether the caller must re-check the length. Those omissions leave the calling agent guessing about the return contract.
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?
Only one parameter and schema description coverage is 100% ('Oversized text to condense below the 10,000-char limit'), so the schema fully documents it. The description adds no syntax, formatting, or content-preservation guidance beyond what the schema already says; baseline 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?
States a specific verb (condenses) plus resource (text) and pins the exact trigger condition (exceeds the 10,000-character translation limit, would otherwise truncate). This is clearly distinct from siblings such as translate_text, optimize_text, and refine_translation, which do not shrink input.
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?
Gives a clear context for use: oversized text that would be truncated in translation. The implicit workflow (run this before translate_text when input is too long) is easy to infer, but no sibling is named and there is no explicit 'do not use when' clause, e.g. for text already under the limit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Translates README and docs content while preserving Markdown structure, links, and code fences.
| Name | Required | Description | Default |
|---|---|---|---|
| tone | Yes | Tone (0=Concise, 100=Detailed) | |
| input | Yes | Input text (min 10 chars) | |
| format | Yes | Output format | |
| context | Yes | Tech context | |
| audience | Yes | Target audience | |
| webSearch | No | Ground generation with real-time web search | |
| managedModel | No | Pin a specific managed model (server-side allowlist) | |
| responseFormat | No | Output structure: prose Markdown, or machine-readable XML | |
| correctionsOnly | No | Write mode: true fixes spelling, grammar and punctuation only; omitted or false improves the whole text (word choice, flow, clarity). Applies to optimize_text and to format "Optimize". | |
| contextProjectId | No | Ground generation on a Context Project's instructions/assets | |
| customInstructions | No | Optional custom instructions |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It does disclose a real behavioral guarantee (structure, links, and code fences are preserved), which is useful and non-obvious, but it says nothing about permissions, rate limits, model selection behavior, or what form the response takes.
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?
A single front-loaded sentence with no filler; every clause (scope plus the three preservation guarantees) 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 an 11-parameter, 5-required tool with no annotations and no output schema, the description covers only the Markdown-preservation angle. The rich schema compensates on parameters, but the description leaves ambiguous why non-translation formats like 'Prompt' and 'Optimize' appear in the format enum, and it says nothing about what the tool returns.
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%, so the schema already documents all 11 parameters including enums and the tone scale. The description adds no syntax or format detail beyond the Markdown-preservation promise, so the baseline 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?
States a specific verb+resource (translates Markdown/README docs) and adds a distinguishing trait: preserving structure, links, and code fences. That implicitly separates it from translate_text, though it never names the sibling to make the distinction explicit.
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?
Usage is only implied by the narrow scope ('README and docs content'). There is no when-to-use statement, no condition for choosing this over translate_text or refine_translation, and no mention of prerequisites despite 5 required parameters.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Translates and optimizes informal text into formal, audience-specific technical specifications. With format "Optimize" it returns the input itself improved, in its own language, instead of a document (set correctionsOnly for corrections only).
| Name | Required | Description | Default |
|---|---|---|---|
| tone | Yes | Tone (0=Concise, 100=Detailed) | |
| input | Yes | Input text (min 10 chars) | |
| format | Yes | Output format | |
| context | Yes | Tech context | |
| audience | Yes | Target audience | |
| webSearch | No | Ground generation with real-time web search | |
| managedModel | No | Pin a specific managed model (server-side allowlist) | |
| responseFormat | No | Output structure: prose Markdown, or machine-readable XML | |
| correctionsOnly | No | Write mode: true fixes spelling, grammar and punctuation only; omitted or false improves the whole text (word choice, flow, clarity). Applies to optimize_text and to format "Optimize". | |
| contextProjectId | No | Ground generation on a Context Project's instructions/assets | |
| customInstructions | No | Optional custom instructions |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It does add real behavioral context: the output shape changes conditionally on format (Optimize returns the improved input in its own language rather than a document), and correctionsOnly switches to a narrow correction pass. Beyond that it says nothing about auth needs, rate limits, or what non-Optimize formats actually return, so the disclosure is partial.
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, purpose front-loaded, with the special-case behavior appended. It is dense but every clause carries information, and the parenthetical about correctionsOnly is a compact pointer rather than 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 an 11-parameter, 5-required tool with no output schema, two sentences is thin. The description never explains what the non-Optimize formats produce, whether any language translation actually occurs despite the name, or how webSearch, managedModel, contextProjectId and responseFormat affect the result, leaving the agent to reconstruct the tool's behavior from the schema alone.
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%, so the baseline is 3 and every parameter (tone, format, context, audience, webSearch, etc.) is already documented in the schema. The description only adds a bit of cross-parameter meaning around format "Optimize" plus correctionsOnly, which the schema's correctionsOnly text largely already covers, so no lift 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 gives a specific verb and resource: it turns informal text into formal, audience-specific technical specifications, and it calls out the exception where format "Optimize" returns the improved input in its own language instead of a document. That is clear enough to act on, but it never distinguishes this tool from the closely related sibling optimize_text (nor from refine_translation/translate_markdown), so the boundary stays fuzzy.
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 implicitly guides usage by explaining that format "Optimize" is the mode for improving the input in place and that correctionsOnly narrows that to corrections. However it never states when to pick this tool over optimize_text or the other translation siblings, and gives no prerequisites or exclusions, leaving the main selection decision to inference.
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.
auth_confirmdetect_languageget_account_infoget_historylist_languagesoptimize_textpatch_translation_notepublish_linkedin_postrefine_translationsummarize_inputtranslate_markdowntranslate_textPay-per-use tool marketplace for AI agents. Search, price-check, and call APIs via MCP.
Text generation over MCP: prose, emails, blog outlines, SQL, humanizing, text diffs, fake data.
Agent-native notes, tasks, dev-docs, vaults, sync & handoffs. MCP + OpenAPI dual surface.
Gemmein docs, API reference, error explainers and a live integration check for coding agents.
Add one secure layer between your agents and this server.
