ToolSnap MCP
Server Details
AI agent microtools. fetch_extract: 98.1% token reduction. $0.02 USDC on Base. 10 free tools.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.2/5 across 21 of 21 tools scored.
Each tool has a clearly distinct purpose: encoding/decoding pairs, different query formats (CSV, JSON, RSS, sitemap), and separate text operations (tokens, stats, diff, regex). No overlap that would confuse an agent.
All tools use a consistent snake_case verb_noun pattern (e.g., base64_encode, csv_query, fetch_extract, pdf_text_extract). The naming is predictable and descriptive throughout.
21 tools is slightly above the typical 3-15 range for a utility server, but each tool serves a well-defined purpose and the count is justified by the breadth of functionality (encoding, text processing, web extraction, parsing).
The toolset covers encoding, decoding, text analysis, web fetching, parsing (CSV, JSON, RSS, sitemap, PDF), hashing, UUID generation, and timestamp conversion. Missing some utilities like hex encoding or string manipulation, but core workflows are well covered.
Available Tools
24 toolsaccount_balanceAInspect
Check the prepaid USDC balance for an address. Free. Returns remaining balance, the discounted prepaid per-call price, and how many paid calls it covers. Deposit once with account_deposit to pay a discounted per-call rate with no 402 round-trip and no gas per call.
| Name | Required | Description | Default |
|---|---|---|---|
| address | Yes | The 0x EVM address whose prepaid balance to look up (the address that funded the deposit). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses behavioral traits: free, returns balance/discounted price/calls, no 402 round-trip, no gas. This is useful context beyond the input schema.
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 with no wasted words, front-loaded with the core purpose. Every sentence adds value.
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 required parameter and no output schema, the description fully explains what it returns and how it relates to the sibling tool, leaving no 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?
The schema already describes the address parameter (100% coverage), and the description adds meaning by specifying it is the address that funded the deposit, clarifying 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 verb 'Check' and the resource 'prepaid USDC balance for an address', and distinguishes it from sibling tools like account_deposit. It is specific and 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?
The description mentions when to use (to check balance) and directs to account_deposit for depositing, giving clear context. It could explicitly state when not to use, but the guidance is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
account_depositAInspect
Open or top up a prepaid balance by depositing USDC on Base via x402 (one on-chain settlement). After depositing, paid tools are debited off-chain at the discounted prepaid price with no per-call 402 and no per-call gas, until the balance runs out. Non-refundable credits. Minimum deposit $0.50. Call with the signed x402 PaymentPayload in _meta["x402/payment"] for the amount you want to deposit (>= minimum); the full deposited amount is credited to the paying address.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses key behaviors: one on-chain settlement, debited off-chain until balance runs out, non-refundable credits, minimum deposit $0.50. It could be more explicit about error handling or auth requirements, but is largely 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 is a single paragraph that front-loads the action, then details benefits and instructions. Every sentence adds value, but could be slightly more concise. It is well-structured for an AI agent.
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 no output schema and zero formal parameters, the description is fairly complete. It explains purpose, usage, constraints, and invocation. It is missing explicit return value or error scenarios, but overall sufficient.
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 has zero formal parameters, and the description provides all necessary parameter semantics: the required x402 PaymentPayload in meta and the amount to deposit. Schema coverage is 100% (no params), so description adds all meaning.
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 action: 'Open or top up a prepaid balance by depositing USDC on Base via x402 (one on-chain settlement).' It specifies the resource (prepaid balance), the method (x402), and differentiates from siblings which are unrelated.
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 gives explicit invocation instructions: 'Call with the signed x402 PaymentPayload in _meta["x402/payment"] for the amount you want to deposit (>= minimum).' It also clarifies the benefit (lower fees) and constraint (non-refundable). However, it does not explicitly state when not to use this tool or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
base64_decodeAInspect
Decode a Base64 string back to UTF-8 text. Handles non-ASCII characters correctly. Use to recover the original text from a Base64-encoded value.
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | The Base64-encoded string to decode. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that the tool handles non-ASCII characters correctly, which is a behavioral trait not covered by annotations (none provided). However, it does not mention error handling for invalid Base64 input or other limitations, leaving some behavioral aspects implicit.
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 at two sentences, with the first sentence immediately stating the core purpose. No wasteful or redundant language, making it easy for an agent to parse quickly.
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, no output schema), the description fully covers the needed information: what it does, how it handles edge cases (non-ASCII), and when to use it. No gaps remain.
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 has 100% coverage with a description for the 'data' parameter. The description adds meaning by specifying the output encoding (UTF-8) and non-ASCII handling, which goes beyond the schema's parameter description.
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 'Decode' and the resource 'Base64 string', and specifies the output as 'UTF-8 text'. This distinguishes it from the sibling tool 'base64_encode' which performs the opposite operation.
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 explicit usage guidance with 'Use to recover the original text from a Base64-encoded value,' indicating when this tool should be invoked. However, it does not explicitly mention when not to use it or alternatives beyond the sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
base64_encodeAInspect
Encode a UTF-8 text string to Base64. Handles non-ASCII characters correctly. Use when you need to embed binary or unicode data in JSON, URLs, or HTTP headers.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The UTF-8 text to encode as Base64. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Mentions 'Handles non-ASCII characters correctly' but doesn't disclose output format or any size limitations. Adequate but not comprehensive.
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 concise sentences, front-loaded with verb and resource, no wasted words. Every sentence adds value.
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?
Simple tool (single parameter), but missing explanation of return value (Base64 string). Output schema absent, so description could have clarified output format.
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%, but description adds value by specifying UTF-8 input and non-ASCII handling, which goes beyond parameter name and type.
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 clearly states action ('Encode a UTF-8 text string to Base64') and resource ('text string'). Distinguishes from sibling tools like base64_decode by specifying encoding direction.
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: 'when you need to embed binary or unicode data in JSON, URLs, or HTTP headers'. Doesn't explicitly exclude other cases, but context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
count_tokensAInspect
Estimate the number of tokens a text will consume when sent to an LLM. Uses a byte-pair encoding approximation compatible with cl100k_base (GPT-4, Claude, and most modern models). Accurate to ±10% on English prose. Returns token count, character count, byte count, and a cost estimate footnote. Use before sending long context to an LLM to avoid surprises.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to count tokens for. | |
| model | No | Model name hint (default "cl100k_base"). Accepted: cl100k_base, gpt-4, gpt-4o, gpt-3.5-turbo, claude, claude-3, claude-sonnet, claude-haiku, claude-opus, text-embedding-ada-002. All currently use the same cl100k_base approximation. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses approximation method, accuracy bounds, and return fields. No annotations provided, so description carries full burden; could mention it does not actually send text to an LLM.
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?
Four sentences, no wasted words. Front-loaded with purpose. Every sentence adds information about accuracy, usage, and return values.
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 no output schema, description covers return fields (token, character, byte count, cost estimate) and accuracy. Sufficient for a simple estimation 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?
Adds context beyond schema: lists accepted model values, notes all use same approximation, and explains the 'text' parameter's role. Schema coverage is 100% but description enriches.
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?
Clear verb 'estimate' with specific resource 'token count for LLM text input'. Differentiates from sibling tools like text_stats by specifying token estimation and compatible models.
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?
States use case 'before sending long context' and warns about surprises. Lacks explicit when-not-to-use or alternatives, but the context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
csv_queryAInspect
Fetch a CSV from a URL (or accept raw CSV text) and query it: select columns, filter rows, sort, and limit results. Returns JSON array of objects by default, or CSV with format=csv. Use instead of loading the full CSV into your context — send only the data you need.
| Name | Required | Description | Default |
|---|---|---|---|
| csv | No | Raw CSV text (alternative to url). | |
| url | No | URL of the CSV file to fetch (http/https). | |
| limit | No | Maximum number of rows to return (default 500, max 5000). | |
| filter | No | Row filter expression: 'column op value'. Operators: = != > >= < <= contains startswith endswith. E.g. 'age > 30' or 'country = US'. | |
| format | No | Output format: 'json' (default) or 'csv'. | |
| select | No | Comma-separated column names to include (e.g. 'name,age,country'). Omit to include all columns. | |
| sort_by | No | Column name to sort by. | |
| sort_dir | No | Sort direction: 'asc' (default) or 'desc'. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses key behaviors (fetch from URL, accept raw text, default JSON output, limit parameters from schema) but omits error handling, rate limiting, or consequences of failed fetches. The description 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 long, front-loaded with the core action and use case. Every sentence adds value with no redundancy or irrelevant 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 8 parameters and no output schema, the description provides a high-level overview covering the main operations. It is sufficient for an agent to understand the tool's purpose, though edge cases and output details are left to the schema.
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 baseline 3. The description summarizes operations and output format, but does not add significant meaning beyond what the schema provides. It complements but does not enhance parameter understanding substantially.
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 'Fetch and query', identifies the resource (CSV from URL or raw text), and specifies operations (select, filter, sort, limit). It distinguishes itself from sibling tools like json_query and fetch_extract by emphasizing that it avoids loading the full CSV into context.
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 a clear usage recommendation ('Use instead of loading the full CSV into your context'), which guides the agent on when to prefer this tool. However, it lacks explicit when-not-to-use or alternative tool mentions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
diff_textAInspect
Compare two text strings and return a unified diff showing additions (+), deletions (-), and context lines. Useful for review agents, changelog generation, and patch creation. Returns '(no differences)' when inputs are identical. Free — no payment required.
| Name | Required | Description | Default |
|---|---|---|---|
| a | Yes | The "before" text (original). | |
| b | Yes | The "after" text (modified). | |
| context | No | Number of unchanged context lines around each change (default 3, max 10). | |
| label_a | No | Label for the "before" file header (default "a"). | |
| label_b | No | Label for the "after" file header (default "b"). |
Tool Definition Quality
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 mentions returning '(no differences)' when identical and notes it is free. However, it does not discuss performance limits, error handling, or exact output format details, which is moderate 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?
The description is only 3 sentences, each adding value: main action, use cases, edge case and cost info. It is front-loaded and concise 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?
The tool is simple with 5 parameters. The description covers purpose, use cases, output format (unified diff), and edge case. Without an output schema, it adequately explains return values. Minor gaps (e.g., error handling) prevent a perfect score.
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 schema already documents all 5 parameters. The description adds general context about the diff output but does not elaborate on parameter semantics beyond what the schema provides. 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 compares two text strings and returns a unified diff with additions, deletions, and context lines. It uses specific verb 'compare' and resource 'text strings', and distinguishes from sibling tools like base64_encode or csv_query.
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 use cases: 'review agents, changelog generation, and patch creation'. This gives clear context on when to use the tool. It does not explicitly state when not to use or list alternatives, but the provided scenarios are sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
extract_structuredAInspect
Fetch a URL and extract structured data matching a JSON Schema — title, author, date, price, description, rating, image, and more. Reads JSON-LD, Open Graph, Twitter Cards, and Schema.org microdata embedded in the page; returns only the extracted JSON object. No LLM required: extraction is deterministic. Ideal for articles, products, recipes, events, and any page using semantic markup.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL to fetch and extract data from. | |
| schema | Yes | JSON Schema (as a JSON string) describing the fields to extract. E.g. {"type":"object","properties":{"title":{"type":"string"},"price":{"type":"number"}}} |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses the deterministic nature, source markup types, and that only extracted JSON is returned. Missing details on error handling, timeouts, redirect behavior, or cases where no data is found.
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 efficient sentences with no redundancy. First sentence captures core function, second details tech stack, third provides usage guidance. Every sentence adds value.
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 2 required params and high schema coverage, description adequately explains extraction mechanics and output format. Minor gap: no output schema or error format specification, but the listing of possible extracted fields partially compensates.
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 are fully described in the input schema (100% coverage). The description reiterates the schema parameter, but adds context about example fields to extract, which is helpful but not essential beyond schema info.
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 it fetches a URL and extracts structured data matching a JSON Schema, listing supported data types and markup formats. It specifically distinguishes from siblings like fetch_extract by highlighting deterministic extraction and semantic markup focus.
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 it's ideal for pages with semantic markup and deterministic extraction, implying contexts where LLM-based extraction (like sibling fetch_extract) is not needed. However, it does not explicitly mention when not to use or name alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fetch_extractAInspect
Fetch a URL and return clean text, stripped of HTML, scripts, styles, and navigation. Benchmark (11 real pages): median 98.1% token reduction (53 820 → 2 001 tokens); saves ~$0.156/call at Sonnet pricing ($3/M tokens) vs loading raw HTML. Break-even at 26 KB pages — virtually all real pages qualify. Deterministic, parallel-safe, zero-setup. Cost: $0.02 USDC on Base. First call free per wallet address.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The URL to fetch and extract text from | |
| maxChars | No | Max characters to return (default 8000, max 32000) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries full burden. It discloses determinism, parallel-safety, zero-setup, and cost. However, it omits error handling (e.g., invalid URLs) and rate limits. Still, it offers substantial 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 front-loaded with purpose and includes relevant benchmarks and cost. It is relatively concise but could separate usage tips from pure description. Each sentence adds value for selection decisions.
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 2 parameters and no output schema, the description covers functionality, cost, and safety. It lacks explicit return format (e.g., returns a string) but overall provides sufficient context for an agent to invoke 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 coverage is 100%, so the description adds limited value beyond the schema. It doesn't elaborate on url or maxChars beyond their basic types. Baseline score of 3 is appropriate when schema fully documents 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 clearly states the tool fetches a URL and returns clean text stripped of HTML, scripts, styles, and navigation. This specific verb+resource combination distinguishes it from siblings like html_to_markdown or webpage_metadata.
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 (for plain text extraction with token savings) but lacks explicit exclusions or alternatives. The benchmark and cost details provide context for cost-sensitive decisions, but no direct comparison to sibling tools is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
hash_textAInspect
Compute a cryptographic hash (SHA-256, SHA-1, or SHA-512) of any text string. Returns lowercase hex. Use for integrity checks, content fingerprinting, or generating deterministic IDs from content.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to hash. | |
| algorithm | No | Hash algorithm to use. Default "SHA-256". | SHA-256 |
Tool Definition Quality
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 accurately describes the output format (lowercase hex) and algorithm options. It could mention determinism or that it does not modify any state, but these are implicit for a pure function.
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 concise sentences: first explains what the tool does, second explains when to use it. No wasted words, front-loaded with essential 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?
For a simple hashing tool with two parameters and no output schema, the description covers all necessary aspects: algorithms, output format, and use cases. No 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 coverage is 100% with descriptions for both parameters. The description adds minimal value beyond the schema, only listing the algorithms and output format. 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?
The description clearly states the tool computes cryptographic hashes (SHA-256, SHA-1, or SHA-512) of text, returning lowercase hex. This distinct functionality differentiates it from sibling tools like base64_encode or json_format.
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 lists use cases: integrity checks, content fingerprinting, or generating deterministic IDs. While it doesn't mention when not to use or alternatives, the context makes it clear this is the correct tool for hashing needs.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
html_to_markdownAInspect
Convert a URL or raw HTML string into clean Markdown. Strips navigation, ads, scripts, and boilerplate; preserves headings, lists, links, code blocks, and emphasis. Use instead of loading raw HTML into context — saves 85–98% of tokens compared to the original page. Accepts either a URL (fetched server-side) or an html parameter with raw HTML.
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | URL to fetch and convert (http:// or https://). | |
| html | No | Raw HTML string to convert directly (alternative to url). | |
| maxChars | No | Max characters of Markdown to return (default 12000, max 50000). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It details stripping navigation, ads, scripts, boilerplate; preserving headings, lists, etc.; and token savings. Mentions server-side fetching for URLs.
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 concise sentences, each serving a purpose: first states action and effects, second provides usage guidance and param clarification. 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?
Covers purpose, behavior, usage, and param details. Missing explicit mention of return format (Markdown string), but given lack of output schema, it's still fairly 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% (all parameters described). The description clarifies mutual exclusivity of url and html, and notes maxChars defaults, but adds limited new meaning beyond schema.
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 converts a URL or raw HTML to clean Markdown, with specific verb and resource. It distinguishes from siblings like fetch_extract by emphasizing the conversion to Markdown and token savings.
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?
Provides explicit when-to-use advice: 'Use instead of loading raw HTML into context — saves 85–98% of tokens.' No explicit when-not or alternatives to siblings, but the use case is well-defined.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
json_formatAInspect
Parse and reformat a JSON string with a configurable indent level (0 = minified, 2 = standard pretty-print). Use to pretty-print, validate, or minify JSON.
| Name | Required | Description | Default |
|---|---|---|---|
| json | Yes | The JSON string to parse and reformat. | |
| indent | No | Indentation spaces (0–8). 0 = minified. Default 2. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It explains the core behavior (parse and reformat with indent) and mentions validation, but does not disclose error handling, edge cases, or return format in detail.
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 function, and efficiently covers key points without padding.
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 the main functionality and use cases, but lacks an explicit statement about return value (e.g., returns the formatted JSON string). It also does not specify behavior on invalid JSON. Adequate but not 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 description coverage is 100%. The description adds some semantic context (e.g., 'parse and reformat' for json parameter) but does not significantly expand beyond what the schema already provides. 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?
The description clearly states the tool's purpose: parsing and reformatting JSON with configurable indent. It explicitly mentions use cases (pretty-print, validate, minify) and distinguishes from siblings like json_query.
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 says 'Use to pretty-print, validate, or minify JSON,' giving clear usage contexts. It does not explicitly exclude alternatives or state when not to use, but the context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
json_queryAInspect
Fetch JSON from a URL (or accept raw JSON) and query it with a JSONPath-lite expression. Supports property access, array indexing, wildcards ([*]), recursive descent (..), and filter expressions ([?(@.price < 10)]). Returns only the matching values as JSON — use instead of loading large JSON payloads into your context.
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | URL returning JSON to query (http/https). | |
| json | No | Raw JSON string to query (alternative to url). | |
| limit | No | Max number of results to return (default 100, max 1000). | |
| query | Yes | JSONPath-lite expression. Examples: '$.users[*].name', '$..price', '$.items[?(@.stock > 0)].id', '$.store.books[0].title' |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It explains the output ('Returns only the matching values as JSON') and mentions the limit parameter, but does not cover error handling, authentication needs, or side effects of network calls.
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 concise sentences that front-load the purpose and then detail capabilities and usage advice. Every sentence adds value; 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 no output schema, the description adequately states that results are matching JSON values. All parameters are covered. However, missing error scenarios or network failure handling slightly reduces completeness.
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 baseline is 3. The description adds value by explaining supported JSONPath features (property access, array indexing, wildcards, recursive descent, filters) and the use of the limit parameter, going beyond schema descriptions.
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 fetches JSON from a URL or accepts raw JSON and queries it with JSONPath-lite expressions. This distinguishes it from sibling tools like csv_query and fetch_extract.
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 using this tool 'instead of loading large JSON payloads into your context,' providing clear context for when to use it. However, it does not specify when not to use it or explicitly name alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
pdf_text_extractAInspect
Fetch a PDF from a URL and extract its text content. Handles FlateDecode-compressed streams (the most common compression in modern PDFs) and RC4-encrypted PDFs that open with an empty password. Works on text-based PDFs (those generated from Word, LaTeX, web, etc.); does not perform OCR on scanned/image-only PDFs. Returns clean plain text — use instead of loading raw PDF bytes into your context.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL of the PDF to fetch (http/https). Must be a PDF file. | |
| maxChars | No | Max characters to return (default 20000, max 100000). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully bears the burden. It discloses supported compression (FlateDecode), encryption handling (RC4 with empty password), and limitation (no OCR). This is adequate but could mention potential errors.
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 and well-structured. The main purpose is in the first sentence, followed by technical details and usage hints. 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 simple interface (2 params, no output schema) and lack of annotations, the description covers key aspects: what it does, its limitations, and a usage recommendation. It is complete enough for an agent to make an informed decision.
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 baseline is 3. The description does not add significant meaning beyond the schema; it reiterates the URL requirement and character limit. No additional context for parameter values 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 explicitly states 'Fetch a PDF from a URL and extract its text content,' clearly identifying the action and resource. It differentiates from sibling tools like fetch_extract and html_to_markdown by specifying PDF text extraction.
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: works on text-based PDFs, not on scanned/image-only PDFs. It advises using this tool instead of loading raw PDF bytes. However, it does not name specific alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
pricingAInspect
Returns the machine-readable pricing menu for this server: which tools are free vs paid, pay-per-call vs discounted prepaid pricing, how to deposit and spend a prepaid balance, payment method, and quantified value (token savings, ROI). Call this first to understand what is available and at what cost before using paid tools.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
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 data without side effects, listing what information is included. It could mention that it is read-only, but the absence of annotations does not penalize; the description provides sufficient 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 three sentences long, with the first sentence stating the core function, the second detailing contents, and the third providing usage guidance. Every sentence is substantive and efficient, 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?
The tool is simple (no parameters, no output schema), and the description covers what is returned and why to use it. It could be more specific about the output format (e.g., JSON), but overall it is complete 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 tool has zero parameters, so the input schema is fully covered (100%). The description does not need to add parameter info, and the baseline for 0 parameters is 4. No additional value 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 clearly states the tool returns the machine-readable pricing menu, listing specific contents like free vs paid tools, deposit/withdrawal, and quantified value. It also distinguishes itself as the tool to call first before using paid tools, differentiating it from siblings like account_balance.
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 says 'Call this first to understand what is available and at what cost before using paid tools,' providing clear context for when to use. However, it does not mention when not to use or specify alternatives, missing the 'when-not/alternatives' criterion for a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
regex_extractAInspect
Run a regular expression against text and return all matches. Supports capture groups, named groups, and multiline input. Returns a JSON array of match objects — each has match (full match) and groups (array of capture groups, or object for named groups). Use for extracting emails, URLs, codes, patterns, or any structured data from unstructured text.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to search. | |
| flags | No | Regex flags string (default "gi"). Common: g=global, i=case-insensitive, m=multiline, s=dotAll. | |
| pattern | Yes | Regular expression pattern (without delimiters). | |
| maxMatches | No | Max matches to return (default 100, max 500). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Describes output format (JSON array with match and groups) and mentions support for multiline input and named groups. Does not cover error behavior (e.g., invalid regex) but is sufficiently transparent for a pure regex function.
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 concise sentences: first states action, second lists capabilities, third describes output and use cases. No wasted words, front-loaded with 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?
With 4 parameters and no output schema, the description covers what the tool does, what it returns, and common use cases. Lacking details on error handling or edge cases (e.g., empty text), but adequate for a regex extraction 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% with descriptions for all 4 parameters. Description adds value beyond schema by explaining the output structure (match vs groups) and clarifying that groups can be array or object for named groups. This supplements the missing output schema.
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 clearly states 'Run a regular expression against text and return all matches', listing specific capabilities like capture groups and named groups, and provides concrete use cases (emails, URLs, patterns). This distinguishes it from sibling tools like json_query or csv_query.
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 for extracting emails, URLs, codes, patterns, or any structured data from unstructured text', giving clear context. Does not explicitly mention when not to use, but the sibling tools imply alternative uses for structured data formats.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
rss_parseAInspect
Fetch and parse an RSS 2.0 or Atom 1.0 feed URL. Returns structured JSON with feed metadata (title, description, language, last-build date) and an array of items (title, link, pubDate, author, categories, description, enclosure). Use instead of fetching raw XML — saves 90%+ of tokens and eliminates XML parsing in the agent. Ideal for news aggregation, content monitoring, and feed-based workflows.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | RSS or Atom feed URL (http:// or https://). | |
| maxItems | No | Max feed items to return (default 20, max 200). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It explains that the tool returns JSON and saves tokens, but does not disclose error handling, rate limits, or behavior on malformed feeds. Adequate but 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 concise (three sentences) and front-loaded with the main action. Every sentence adds value: function, benefit, use cases. No unnecessary 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 simple tool with two parameters and no output schema, the description covers the main functionality, output structure, and usage context well. It could mention error behavior or timeouts but is largely 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% and both parameters have descriptions. The description adds no new parameter details beyond the schema, though it lists output fields which provides context. Meets baseline for a tool with full 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 fetches and parses RSS 2.0 or Atom 1.0 feeds, returning structured JSON with feed metadata and items. It explicitly distinguishes from fetching raw XML, differentiating it from sibling tools like fetch_extract and sitemap_parse.
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 using this tool instead of fetching raw XML, citing token savings and elimination of parsing. It also lists ideal use cases (news aggregation, content monitoring, feed-based workflows), providing clear context for when to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
sitemap_parseAInspect
Fetch and parse an XML sitemap (urlset or sitemapindex). Returns structured JSON: for urlsets — array of URLs with loc, lastmod, changefreq, priority, plus image/news sitemap extensions; for sitemapindex — list of child sitemap URLs. Use to enumerate all pages of a site, find recently updated content, or build a crawl queue — without loading raw XML into context.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Sitemap URL (http:// or https://). Accepts sitemap.xml, sitemap_index.xml, or any XML sitemap. | |
| maxUrls | No | Max URLs to return from a urlset (default 100, max 1000). Has no effect on sitemapindex (all child sitemaps are always returned). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description carries full burden. It discloses return structure (structured JSON with fields), behavior for urlset vs sitemapindex, and maxUrls constraint. Does not mention error handling, but 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?
Two sentences, no filler, front-loaded with primary verb and resource. Every sentence adds value.
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?
Complete description for a sitemap parsing tool: covers input, output, both sitemap types, parameter edge cases, and use cases. No output schema needed given the clear description.
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%. Description adds value by explaining that maxUrls has no effect on sitemapindex, and describes return structure, exceeding 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 it 'Fetch and parse an XML sitemap (urlset or sitemapindex)' and details the output for each type. It distinguishes itself from sibling tools like rss_parse and fetch_extract by focusing on sitemaps.
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 cases: 'enumerate all pages of a site, find recently updated content, or build a crawl queue'. Lacks explicit when-not-to-use, but context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
text_statsAInspect
Count characters, words, lines, and sentences in a text string. Returns a JSON object. Use when you need to analyse or report on the size and structure of a body of text.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to analyse. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries burden. It mentions returns a JSON object, but does not disclose edge cases (e.g., empty string, Unicode handling) or potential limits. 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?
Two sentences, front-loaded with action, every sentence provides value. No 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 tool with one parameter and no output schema, the description covers purpose, output type, and usage context. Missing specifics on metric definitions (e.g., what constitutes a word), but overall adequate.
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 single parameter 'text'. Description does not add any meaning beyond the schema's description. 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?
The description clearly states it counts characters, words, lines, and sentences in a text string, which is a specific and unique purpose among siblings like base64_decode or csv_query.
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 says 'Use when you need to analyse or report on the size and structure of a body of text,' providing clear context. It lacks explicit when-not-to-use or alternatives, but the sibling list naturally differentiates.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
timestamp_convertAInspect
Convert between Unix timestamps (seconds) and ISO 8601 date strings. Auto-detects direction: numbers → ISO, date strings → Unix seconds. Accepts Unix in seconds or milliseconds (auto-detected). Use when you need to convert epoch values to human-readable dates or vice-versa.
| Name | Required | Description | Default |
|---|---|---|---|
| to | No | "iso" to force output as ISO 8601, "unix" to force output as Unix seconds. Omit to auto-detect. | |
| input | Yes | The value to convert. Either a Unix timestamp (as a number or numeric string, seconds or ms) or an ISO/date string like "2024-01-15T10:30:00Z". |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses auto-detection of direction and unit (seconds vs milliseconds), and explains the role of the 'to' parameter. It does not cover error handling or edge cases, but for a simple conversion tool, this is sufficient and consistent.
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, no unnecessary words. Front-loaded with main function, followed by key behavioral details. 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 and no output schema, the description covers main usage, parameter behavior, and auto-detection. It does not explicitly describe the return format, but it's inferable. Overall, it's complete enough for an agent to use 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%, so baseline is 3. The description adds value by explaining auto-detection behavior and that input accepts ms, which goes beyond the schema's simple descriptions. It clarifies the 'to' parameter's purpose in forcing output format.
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 converts between Unix timestamps and ISO 8601 date strings, with a specific verb 'Convert' and resource 'timestamps/date strings'. It distinguishes itself from sibling conversion tools (e.g., base64, json) by focusing on timestamps, and mentions auto-detection of direction.
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 says 'Use when you need to convert epoch values to human-readable dates or vice-versa,' providing clear context. It also explains the 'to' parameter for forcing direction, but does not explicitly mention when not to use it, though siblings are unrelated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
url_decodeAInspect
Decode a percent-encoded URL string back to plain text (decodeURIComponent). Use to recover the original text from a URL-encoded value.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The URL-encoded string to decode. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full burden. It names the underlying function but does not disclose error handling, malformed input behavior, or return format. Given simplicity, it is adequate but lacks extra 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?
Two sentences, no fluff, front-loaded with the purpose. Every sentence adds value.
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 1-param tool without output schema, the description is mostly complete. It lacks mention of the return value (decoded string), which is minor but expected.
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% (parameter 'text' already described). The tool description adds no new meaning beyond the schema, 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?
The description clearly states the tool decodes a percent-encoded URL string (verb+resource) and references the specific JS function decodeURIComponent, distinguishing it from url_encode and other 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?
The description says 'Use to recover the original text from a URL-encoded value,' providing clear context for when to use it, but does not mention when not to use it or point to alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
url_encodeAInspect
Percent-encode a string for safe inclusion in a URL query parameter or path segment (encodeURIComponent). Use when building URLs that contain special characters, spaces, or non-ASCII text.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to URL-encode. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'Percent-encode' and 'encodeURIComponent', implying standard URL encoding behavior. However, it does not disclose details such as which characters are preserved or any potential side effects. This is adequate but not highly 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 concise sentences: one explaining the function and one providing usage guidance. Every sentence earns its place 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?
With a single parameter and no output schema, the description covers the essential aspects: what the tool does, when to use it, and the input parameter. The output (URL-encoded string) is implied but not explicitly stated, which is a minor gap. Overall, it is sufficiently complete for 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?
The schema covers 100% of parameters with a basic description. The tool description adds meaningful context: the purpose (safe inclusion in URLs) and the specific encoding function (encodeURIComponent). This enhances parameter understanding beyond the schema.
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 action ('Percent-encode a string') and resource ('a string for safe inclusion in a URL query parameter or path segment'). It also references the standard function encodeURIComponent, distinguishing it clearly from siblings like url_decode and other unrelated 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 explicit guidance on when to use the tool: 'Use when building URLs that contain special characters, spaces, or non-ASCII text.' It does not mention when not to use or suggest alternatives, but the context is clear enough 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.
uuid_generateAInspect
Generate one or more random UUID v4 values. Use when you need unique identifiers for records, sessions, tokens, or any entity requiring a globally unique ID.
| Name | Required | Description | Default |
|---|---|---|---|
| count | No | Number of UUIDs to generate (1–100, default 1). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears the full burden. It mentions randomness and UUID v4 but does not disclose other behavioral traits such as idempotency, side effects, or authentication requirements.
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 action, and contains no unnecessary words. It is 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?
The description lacks information about the return value format. While the tool is simple, the absence of an output schema means the description should hint at what is returned (e.g., a string or list). This is adequate but not fully 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% for the single parameter 'count', which includes a description and constraints. The description does not add new meaning beyond the schema, 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?
The description clearly states the tool generates random UUID v4 values, specifying the exact function. It is distinct from sibling tools like hash_text or base64_encode, 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 explicitly advises using this tool when unique identifiers are needed, providing clear context. However, it does not mention when not to use it or list alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
webpage_metadataAInspect
Fetch a URL and extract its metadata: title, meta description, Open Graph tags (og:title, og:image, og:type…), Twitter Card tags, canonical URL, robots directive, author, keywords, JSON-LD structured data, and lang/charset. Returns a structured JSON object. Much cheaper than loading the full page — ideal for link previews, SEO audits, and content classification.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL to fetch (http:// or https://). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses it extracts specific metadata and returns structured JSON, and mentions cost advantage. No annotations, so description carries burden. However, it does not discuss rate limits, auth needs, or size limits, which are moderate 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?
Two sentences: first lists metadata types efficiently, second adds value statements. Every sentence earns its place; no 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?
For a simple tool with one param and no output schema, the description adequately covers extracted metadata and use cases. Could optionally mention return format details, but current is sufficient.
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 has one parameter 'url' with description. Description reinforces the tool's purpose but adds little beyond schema. Schema coverage is 100%, 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?
The description clearly states it fetches a URL and extracts metadata, listing specific types (title, meta, Open Graph, Twitter, etc.). It distinguishes from siblings like fetch_extract which loads full pages.
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?
Provides use cases: 'link previews, SEO audits, and content classification' and notes it's 'much cheaper than loading the full page', giving context for when to use. Lacks explicit when-not-to-use or alternative tool names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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