20 blogging, brand-voice, copywriting, video and social skill products. 6 of 8 free.
Analyze a draft for readability, passive voice, cliches, and hedging. FREE.
Also measures sentence variety and keyword density, and produces a prioritized fix list. Typical input {"text": ""} returns {"readability_grade": 9.2, "passive_voice_count": N, "cliches_found": [...], "hedging_words": [...], "sentence_count": N, "avg_words_per_sentence": N, "top_repeated_words": [...], "priority_fixes": ["..."]}.
Use on body prose to find readability and style problems. Not for ranking titles (headline_analyzer) and not for platform limits (social_length_check). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "empty text"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The draft to analyze — at least one full sentence; plain text, any length. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. The description reinforces this ('Every call is read-only and idempotent') and adds critical context about error behavior: on invalid input it returns an error object rather than a protocol error, which is beyond what annotations cover.
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 somewhat long but well-structured with clear sections: what it does, typical input/output, usage guidance, error behavior. Every sentence adds value. Could be slightly more concise, but still 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 has only one parameter with full schema coverage and an output schema, the description fully covers the tool's purpose, usage, error behavior, and output format. It includes a representative output example and error handling details, leaving no gaps for an agent.
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% and the description echoes the schema requirement ('at least one full sentence; plain text, any length'). The description adds a typical input example, but this is a minor addition. Baseline 3 is appropriate as schema already explains the parameter well.
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 specific verbs and resources: 'Analyze a draft for readability, passive voice, cliches, and hedging.' It clearly distinguishes from siblings by stating 'Not for ranking titles (headline_analyzer) and not for platform limits (social_length_check).'
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?
Explicitly states when to use ('Use on body prose to find readability and style problems') and when not to use ('Not for ranking titles... Not for platform limits...'). Also provides guidance on error handling and retry safety, making it easy for an agent to decide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Load a product's free gateway skill with its complete instructions. FREE.
Typical input {"slug": "brand-voice"} returns {"slug": "brand-voice", "skill": "", "instructions": ""}.
Returns exactly one skill - the product's free gateway skill - chosen automatically from the slug, with no plan required. Use when the caller wants usable instructions immediately. Not for the product's other skills: those are named and need get_full_skill with a skill_name, which requires a paid plan. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "unknown slug ''. Use list_products."}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Product slug exactly as returned by list_products, e.g. "brand-voice". |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds significant behavioral context beyond the annotations. It details error handling behavior ('never raises a protocol error — it returns {"error": ...}') and confirms idempotency and read-only nature. All behavioral traits are consistent with annotations, and no contradictions exist.
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 concise, well-organized, and front-loaded with the core purpose. It uses short paragraphs with clear sections: purpose, example, usage guidelines, error handling. Every sentence adds value without redundancy.
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 (single parameter, clear output schema), the description covers all needed aspects: purpose, input format, return value structure, usage boundaries, error handling, and behavioral guarantees (read-only, idempotent). Nothing essential is missing.
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 baseline is 3. The description adds a concrete example input and output ({"slug": "brand-voice"} returns specific fields), which clarifies the parameter's purpose and expected format. This extra semantic context raises the score 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 tool loads a product's free gateway skill with complete instructions. It uses a specific verb ('load') and resource ('free gateway skill'), and distinguishes from siblings by mentioning that other skills require 'get_full_skill'. This provides excellent purpose clarity.
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 explicitly states when to use this tool ('Use when the caller wants usable instructions immediately') and when not to ('Not for the product's other skills'). It directly contrasts with the sibling 'get_full_skill' and explains that the other tool requires a paid plan. This provides clear usage guidelines with specific alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Load one product in full: its persona plus every paid skill. PREMIUM (license).
Typical input {"slug": "brand-voice"} returns {"slug": ..., "name": ..., "persona": "", "skills": [{"name": ..., "instructions": ...}, ...], "free_skill": {...}}.
Returns persona plus every skill for one product. Use when the caller wants the whole product. Not for a single skill (get_full_skill) and not for a free look, which list_products and get_free_skill provide with no plan. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "unknown slug ''"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Product slug exactly as returned by list_products, e.g. "brand-voice". |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint, idempotentHint), the description adds critical behavior: error handling never raises protocol errors but returns structured error objects with fix guidance. It confirms read-only and idempotent nature with explicit retry safety. Also discloses license restriction (PREMIUM) not present in annotations.
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 about 150 words, front-loaded with the core purpose, followed by example, usage guidance, and error handling. It is well-structured and each sentence earns its place. A slight trim could be possible, but it remains efficient and scannable.
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 has one parameter and an output schema, the description fully covers input, output (high-level structure shown), error behavior, licensing, and safe retry. It leaves no major gaps for an agent to understand invocation, safety, and alternatives.
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 'slug', with a detailed description. The description adds value by giving a typical input example and showing the expected output structure, which helps the agent understand parameter usage in context. However, the schema already carries the parameter description well, so the incremental semantic addition is moderate.
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 loads one product in full, including persona and every paid skill. It differentiates from siblings by explicitly noting when to use (whole product) and when not to (single skill via get_full_skill, free look via list_products/get_free_skill). The verb 'load' plus specified resource makes purpose unambiguous.
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?
Explicitly states 'Use when the caller wants the whole product' and lists alternatives: Not for a single skill (get_full_skill), not for a free look (list_products/get_free_skill). It also mentions the PREMIUM license requirement, giving clear context and exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Load one paid skill's complete instructions from a product. PREMIUM (license).
Typical input {"slug": "brand-voice", "skill_name": "Voice Audit"} returns {"slug": ..., "skill": ..., "instructions": ""}.
Returns one named skill, selected by skill_name. Use when the caller wants one specific paid skill. Not for the free gateway skill, which get_free_skill returns with no plan, and not for every skill at once (get_full_product). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "unknown slug ''"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Product slug exactly as returned by list_products. | |
| skill_name | Yes | Exact skill name as listed in that product's "skills" array from list_products. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint, idempotentHint), the description adds crucial behavior: error handling never raises a protocol error but returns a structured error object, and every call is safe to retry. This gives the agent concrete recovery instructions not present in annotations.
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 reasonably concise and well-structured, with example, usage guidance, error info, and behavioral notes in separate sentences. However, it could be slightly tightened; for instance, the fallback 'after correcting the input it is always safe to retry' is redundant given idempotentHint.
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 has an output schema, the description covers what the agent needs: input semantics, usage boundaries, error format, and behavioral guarantees (read-only, idempotent, safe retry). The example return structure matches typical usage. No major gaps.
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 clear descriptions for both 'slug' and 'skill_name'. The description adds an example and a note that skill_name selects one skill, but the schema already provides the core semantics (product slug, exact skill name). Minimal extra value.
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: 'Load one paid skill's complete instructions from a product.' It specifies the scope (paid skill, not free or all skills) and provides an example input/output. It distinguishes from sibling tools like get_free_skill and get_full_product.
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 explicitly advises when to use this tool ('Use when the caller wants one specific paid skill') and when not to, referencing siblings: 'Not for the free gateway skill... and not for every skill at once (get_full_product).' This provides clear context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Score and rank up to 20 headlines, best first. FREE.
Scores 0-100 on length, power/emotion words, numbers, structure, and clarity. Typical input {"headlines": ["7 Mistakes New Writers Make", "On Writing"]} returns {"ranked": [{"headline": ..., "score": 85, "notes": ["good length", "contains number"]}, ...]}.
Use when several title options need ranking. Not for email subject lines, which the outbound server's subject_line_scorer judges against deliverability signals, and not for body prose (analyze_writing). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| headlines | Yes | List of candidate headlines as plain strings; only the first 20 are scored. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint as true/false. The description adds valuable behavioral context: it discloses that errors never raise a protocol error but return an error object, and that every call is safe to retry after correction. This goes beyond the annotations.
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 compact, front-loading the key purpose ('Score and rank up to 20 headlines, best first. FREE.'), then providing a quick example, usage guidance, and error behavior. Every sentence serves a distinct purpose with no wasted words.
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 low complexity (one parameter, simple input/output), the description fully covers purpose, usage guidelines, behavioral traits, error handling, and sample response. The output schema exists, so return values are already documented. The description is 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?
Schema coverage is 100%, providing a clear description of the 'headlines' parameter as an array of strings with a limit of 20. The description adds meaning by showing a typical input example and clarifying that only the first 20 are scored, which complements the schema. No additional parameter details are needed.
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 specific verbs ('score and rank') and resource ('headlines'), clearly stating it operates on up to 20 headlines and returns them best-first. It distinguishes itself from siblings by mentioning what it does not handle (email subject lines for subject_line_scorer, body prose for analyze_writing).
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 explicitly states when to use: 'when several title options need ranking.' It also clearly states when not to use: 'Not for email subject lines... or for body prose,' and names the alternative tools (subject_line_scorer, analyze_writing).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List every product in the Creator Studio line with its included skills. FREE.
Takes no arguments. Returns a list of 20 product objects, each {"slug": "brand-voice", "name": ..., "tagline": ..., "skills": ["Skill A", ...], "free_skill": "Gateway Skill Name"}. Use the returned slug values with get_free_skill, get_full_product, or get_full_skill.
Returns metadata only - no persona text and no skill instructions. Use when the caller wants to see what this server covers. Not for keyword search across the whole 138-product catalog, which the catalog server's search_catalog does, and not for instructions the caller can act on (get_free_skill). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description adds rich context beyond annotations: exact return structure (list of 20 objects with shape), notes that it returns metadata only (no persona or instruction text), error behavior (returns error object, never protocol error), and reaffirms read-only/idempotent nature. This goes well beyond the readOnlyHint and idempotentHint annotations.
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?
Description is front-loaded with purpose, then free, then no args, then return structure, then usage guidance, then exclusions, then error handling, then safety. Every sentence earns its place and the ordering is logical. No fluff.
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 has no parameters and rich annotations, the description covers all needed angles: what it lists, what it returns, how to use returned values, what it does not do, error contract, and safety guarantees. The presence of an output schema further reduces burden, but the description already provides a complete picture.
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?
Input schema has zero parameters and description explicitly states 'Takes no arguments.' That is the clearest possible addition. Additionally, description details the return structure, which further helps the agent understand what the tool produces.
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?
Description states a specific verb-resource pair ('List every product in the Creator Studio line with its included skills') and distinguishes from sibling tools by noting it only returns metadata and is not for keyword search or actionable instructions.
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?
Explicitly tells when to use ('when the caller wants to see what this server covers') and when not to use ('Not for keyword search... not for instructions the caller can act on'), even naming an alternative tool (search_catalog from catalog server).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Estimate reading and speaking time plus basic content stats. FREE.
Typical input {"text": ""} returns {"words": 1200, "reading_minutes": 5.2, "speaking_minutes": 8.6, "paragraphs": 14, "fit": "newsletter/blog"}.
Use when length and pacing are the question. Not for writing quality or grade level (analyze_writing). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| wpm | No | Silent reading speed in words per minute; values below 60 are treated as 60. Default 230. | |
| text | Yes | The content to measure, plain text. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds valuable context: 'Every call is read-only and idempotent, so after correcting the input it is always safe to retry.' It also discloses error behavior (returns error object instead of protocol error). No contradiction.
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?
Concise at ~100 words, efficiently structured: purpose, example, usage guidance, error handling, idempotency note. Every sentence serves a purpose, and the most important information is front-loaded.
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?
The description covers all necessary aspects: purpose, typical usage, when to use/not use, error behavior, and idempotency. Given the presence of an output schema (not shown), no further return value details are needed. Complete for this 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?
Schema coverage is 100%, so the baseline is 3. The description does not add new semantic meaning for parameters beyond what the schema provides. The example shows typical input but doesn't explain parameter details or constraints.
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 ('estimate') and resource ('reading and speaking time plus basic content stats'). It provides a concrete example and distinguishes from the sibling 'analyze_writing' 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?
Explicitly states when to use ('Use when length and pacing are the question') and when not to use ('Not for writing quality or grade level'), naming the alternative tool. Also describes error handling behavior and retry safety.
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. Dates show when Glama detected each change.
analyze_writingget_free_skillget_full_productget_full_skillheadline_analyzerlist_productsreading_timesocial_length_checkClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
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After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
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For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
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Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
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7 outreach, sequencing, call-coaching and CRM-hygiene skill products. 6 of 8 free.
20 inbox, calendar, travel, meals and family-logistics skill products. 7 of 9 free.
30 finance, bookkeeping, legal-ops and SMB operations skill products. 6 of 8 tools free.
7 research navigation, thesis, note-taking and citation skill products. 6 of 8 free.
Add one secure layer between your agents and this server.
Each tool targets a distinct function: writing analysis, headline ranking, reading time, social length check, product listing, free skill retrieval, full product retrieval, and paid skill retrieval. There is no overlap, and the descriptions clearly differentiate them.
Tool names use mixed conventions: imperative verbs (get_, list_) for product tools, but descriptive nouns for writing tools (headline_analyzer, reading_time, social_length_check). While not chaotic, the lack of a uniform pattern could cause minor confusion.
With 8 tools covering two related areas (writing analysis and product catalog), the count is well-scoped. Each tool serves a clear purpose without redundancy, fitting the server's 'creator' theme.
The writing tools cover major content needs (draft quality, headlines, reading time, platform limits). The product tools provide complete read-only access to the catalog. A minor gap is the lack of search or filtering, but list_products returns all 20 products, which is adequate.
social_length_checkSocial Length CheckARead-onlyIdempotent Inspect
Check a post against every major platform's length limit at once. FREE.
Covers X, LinkedIn posts and headlines, Instagram captions, Threads, YouTube titles, and meta descriptions. Typical input {"text": ""} returns {"chars": N, "platforms": {"x_post": {"limit": 280, "fits": true, "over_by": 0}, ...}}.
Use before posting, to catch truncation. Not for readability (analyze_writing) and not for reading duration (reading_time). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
Output Schema
No output parameters
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds valuable context: 'Every call is read-only and idempotent, so after correcting the input it is always safe to retry' and describes the non-error-raising behavior ('returns {"error": ...}'). No contradiction with annotations.
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 concise with three focused paragraphs: purpose/platforms, input/output example, and usage/error handling. Every sentence provides necessary information with no redundancy or fluff.
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 (one parameter, clear output schema), the description covers purpose, platforms, usage, error handling, and safety. It is fully self-contained and leaves no ambiguity about what the tool does or how to use 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 coverage is 100% with a clear parameter description. The description adds value by showing the typical input format, expected output structure, and error format, which goes beyond the schema's definition. However, the schema already adequately describes the parameter, so a slight premium is given for the added context.
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 checks a post against every major platform's length limit at once, listing specific platforms (X, LinkedIn, Instagram, Threads, YouTube, meta). This distinguishes it from siblings like analyze_writing and reading_time, which have 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?
Explicitly says 'Use before posting, to catch truncation' and provides when-not-to-use: 'Not for readability (analyze_writing) and not for reading duration (reading_time).' This gives clear guidance on selection vs alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.