JSON/YAML, regex, diff, JWT, SQL dialects — the keyless millisecond ops an agent needs mid-task.
Scored across 14 tools
Most tools have clear, separate purposes: diff, redact, regex, JWT, SQL, timezone, units, QR, screenshot. The main overlap is between json_format and validate_json, since both parse and validate JSON, though their outputs differ enough to be workable.
The majority follow a clear snake_case verb_noun or noun_verb pattern like diff_text, transpile_sql, and timezone_convert. sql_from_description and what_can_you_do break the pattern, but the rest is predictable and readable.
14 tools is well-scoped for a general-purpose developer utility server. Each tool covers a distinct practical need, and the count does not feel bloated for the breadth of features offered.
The toolkit covers a broad range of everyday dev utilities: text diffing, redaction, regex, JSON/YAML, SQL, time, units, JWT, QR, and screenshots. Some common basics like base64, hashing, or URL encoding could be useful additions, but the surface is complete enough for its stated purpose.
Returns which lines were added and which were removed, with line numbers — computed with a longest-common-subsequence, not guessed by a model. Use to compare two versions of a config, a document, or any command output, instead of asking an LLM to eyeball two blobs and hoping it notices.
| Name | Required | Description | Default |
|---|---|---|---|
| a | Yes | The first (before) text. | |
| b | Yes | The second (after) text. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool readOnly and idempotent. The description adds valuable behavioral context by disclosing the LCS algorithm and emphasizing that results are deterministic ('not guessed by a model'). This goes beyond the structured annotations without contradicting them.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the primary outcome, then the use case and rationale. Every sentence contributes essential information with zero redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-string tool with full schema coverage, rich annotations, and an output schema, the description provides complete context: what it returns, how it computes it, and when to use it. No additional explanation of return values is needed given the output 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%, with each parameter explicitly described as 'first (before) text' and 'second (after) text'. The description reinforces the directional comparison but adds little new parameter-level meaning beyond the schema already provides.
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 opens with 'Returns which lines were added and which were removed, with line numbers', clearly specifying the verb and output. The phrase 'computed with a longest-common-subsequence, not guessed by a model' and title 'line by line' differentiate it from sibling tools like diff_tables and from ad-hoc LLM comparison.
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 to compare two versions of a config, a document, or any command output'. It also names the alternative to avoid: 'instead of asking an LLM to eyeball two blobs and hoping it notices', which gives both a positive use case and a clear avoid-this-approach.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Validate and pretty-print JSON (2-space indent). Reports the error location if invalid.
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | ||
| json | No | ||
| indent | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses that it validates and pretty-prints, and reports error location on invalid input, which is useful. However, it does not mention whether it modifies input, requires network access, or any side effects. It does not contradict annotations (there are none), and the description is accurate about its core behavior.
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 concise sentence, front-loaded with the purpose and a key detail (error location). It earns its place 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?
The tool has 3 parameters with no schema descriptions, no annotations, and no output schema. The description is too sparse to guide the agent on how to provide input (url vs json), what the output structure looks like, or any edge cases. It covers the basic functionality but lacks sufficient detail for effective invocation.
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 0%, and the input schema has three properties (url, json, indent) with no descriptions. The description mentions the indent (2-space) but does not clarify the purpose of 'url' or 'json' parameters, nor how they relate (e.g., one or the other?). The description adds minimal meaning beyond the schema, leaving ambiguity.
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 validates and pretty-prints JSON with a 2-space indent, and reports error location if invalid. This distinguishes it from siblings like diff_text, redact_text, and what_can_you_do, though it does not explicitly differentiate from validate_json or json_yaml, but the pretty-print and error location aspects are specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for validating and formatting JSON, but provides no explicit guidance on when to choose this over validate_json or json_yaml. It does not mention when not to use it or alternative tools. The mention of error location could be useful for debugging, but the context is not fully developed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Converts JSON to YAML or YAML to JSON. It works out which one you gave it, so you do not have to say. A parse failure comes back with the parser message instead of silently producing something that looks fine and is not. Use when a config, a CI file, or a Kubernetes manifest needs to be in the other format.
| Name | Required | Description | Default |
|---|---|---|---|
| to | No | Optional: "json" or "yaml" to force the direction. | |
| text | Yes | The JSON or YAML content. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
While annotations already indicate readOnlyHint and destructiveHint, the description adds valuable behavioral context: auto-detection of direction and safe error handling (returns parser message instead of silently producing wrong output). This goes beyond the structured data.
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 four sentences, each serving a purpose: action, auto-detection, error behavior, and use cases. It is front-loaded and free of 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 conversion tool, the description covers the operation, auto-detection, error handling, and example use cases. With output schema present and annotations covering safety, nothing significant 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 coverage is 100%, so baseline is 3. The description adds meaning by explaining that 'to' is optional because auto-detection handles the direction, and it clarifies that 'text' contains the content. This supplements the schema effectively.
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 'Converts JSON to YAML or YAML to JSON' with a specific verb and resource. It also distinguishes itself from sibling tools like validate_json by focusing on conversion rather than validation.
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 use cases: 'Use when a config, a CI file, or a Kubernetes manifest needs to be in the other format.' It does not explicitly mention alternatives, but the context is clear and sufficient for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Decodes the header and payload of a JWT and reports issued-at / expiry as readable timestamps plus seconds remaining. The signature is NOT verified and the response says so — decoding is fine for debugging a token you already hold, but never treat these values as proof of anything; verification needs the secret and belongs in your own service.
| Name | Required | Description | Default |
|---|---|---|---|
| token | Yes | The JWT string. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly/idempotent, but the description adds crucial context: the signature is NOT verified, the response explicitly says so, and decoding should not be treated as proof. This goes beyond basic annotations and is valuable for safe usage.
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: the first states the core function, the second adds the critical limitation, and the third reinforces safe usage. No wasted words and 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?
Given the simple one-parameter schema and presence of an output schema, the description fully covers the tool's behavior including output contents (timestamps, seconds remaining) and the security caveat. It is complete for a debugging 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 already provides 100% coverage with a clear description for the single token parameter. The description does not add further parameter-level detail, so the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool decodes JWT header and payload, reports timestamps and seconds remaining, and explicitly notes the signature is not verified. This is specific and distinguishes it from any other sibling 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?
The description explicitly says decoding is fine for debugging a token you already hold, and warns never to treat values as proof, directing verification to a service with the secret. This provides clear when-to-use and when-not-to-use context without needing a named sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Any text or URL → a QR code PNG you can embed or download. Stateless URL form: https://ainetcafe.com/i/qr/
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text or URL to encode. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is known. The description adds value by specifying the output is a downloadable/embeddable PNG and providing the stateless URL pattern, beyond what annotations convey.
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-loads the core functionality, and includes a useful URL example. Every sentence earns its place with zero 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 one-parameter tool with an output schema, the description covers everything needed: purpose, output format, and an alternative access method. No significant 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?
Schema coverage is 100% with the 'text' parameter well described as 'The text or URL to encode.' The description reinforces that any text works but does not add substantial 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 explicitly states the tool converts any text or URL into a QR code PNG, with a clear verb and resource. It distinguishes itself from sibling tools (e.g., diff_text, json_format) by its specific function and mentions the output 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 clearly implies when to use the tool (whenever a QR code is needed) and mentions the stateless URL form as an alternative delivery method. It does not explicitly name exclusions or alternatives, but context with siblings makes the usage obvious.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Strip emails, phone numbers, ID numbers, API keys, private keys, JWTs, card numbers and IPs out of text, returning the redacted text plus a mapping table to restore them afterwards. Rule-based only — no model sees the input. The same value always maps to the same placeholder, so the answer can be restored.
| Name | Required | Description | Default |
|---|---|---|---|
| only | No | Optional comma-separated subset, e.g. "EMAIL,API_KEY,PRIVATE_KEY". | |
| text | Yes | The text to redact. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description goes well beyond annotations by disclosing that processing is rule-based, no model sees input, and same values map to the same placeholders for restorability. These details add meaningful behavioral context 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?
Two compact sentences, front-loaded with the core action and return value, then adding a key behavioral guarantee. No filler or repetition of schema/annotations.
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 two-parameter tool with a rich output schema and strong annotations, the description fully covers the core function, privacy model, deterministic mapping, and restoration capability. It is complete for an agent to select and 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?
Input schema covers both parameters with clear descriptions (text, optional 'only' subset), so baseline is 3. The description adds no extra parameter-level details, but it does list the entity types that will be redacted by default, which indirectly informs the 'only' filtering behavior.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Strip') and enumerates the exact resource types (emails, phone numbers, ID numbers, API keys, etc.), making it clear what the tool does. It also explicitly differentiates from sibling tools like diff_text and json_format by focusing on redaction and returning a restoration mapping.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'before sending text on' establishes a clear use case, and 'Rule-based only — no model sees the input' provides important context about when this is preferable. It does not explicitly name alternative tools for exclusion, but the sibling list contains no other redaction tool, so distinction is inherent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Runs a regular expression against sample text and returns every match with its position and capture groups (named groups included). Use before wiring a pattern into code, instead of guessing whether the escaping survived the trip through JSON and the shell.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to test against. | |
| flags | No | Optional flags, e.g. "gi". Default "g". | |
| pattern | Yes | The regular expression, without surrounding slashes. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=false, idempotentHint=true, destructiveHint=false, covering safety. The description adds behavioral detail about the return output: every match with position and capture groups, including named groups. This goes beyond annotations and provides useful context without contradicting them.
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, each earning its place. The first states the core action and output; the second gives a concrete usage scenario. No redundant phrasing or 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?
Given the tool's modest complexity, the description covers purpose, usage, and returns. An output schema exists to document the return structure, and annotations cover side-effect safety. The description is sufficient for an agent to select and 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?
The input schema provides descriptions for all three parameters (pattern, text, flags) with 100% coverage, including notes like 'without surrounding slashes' and default for flags. The description does not need to add much parameter-level detail, so a baseline of 3 is appropriate. It does hint at escaping concerns, but that's usage guidance, not parameter semantics.
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 starts with a specific verb ('Runs') and identifies the resource ('regular expression against sample text') and outcome ('returns every match with its position and capture groups'). The title 'Does this regex match — and what does it capture?' reinforces the purpose and distinguishes this from sibling tools like diff_text or 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?
Explicitly states when to use: 'Use before wiring a pattern into code, instead of guessing whether the escaping survived the trip through JSON and the shell.' It provides clear context and a concrete use case, though it does not name an alternative tool explicitly. This is sufficient guidance for an AI agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Full-page screenshot of any URL → PNG. The same job screenshot APIs charge $7-79/month for.
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | ||
| width | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must disclose behavioral traits, but it only mentions 'full-page' and PNG output. It omits details such as rendering behavior, handling of dynamic content, timeout limits, error scenarios, or authentication requirements. The pricing remark is irrelevant to behavior and does not contribute to transparency.
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 brief (two sentences) but the second sentence containing the pricing comparison is irrelevant to tool functionality and adds noise. The core idea is clear, but the extraneous marketing line prevents a higher score.
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 nature of the tool belt (no output schema, minimal annotations), the description should at least explain parameter usage and any limitations. It does not mention width semantics, error handling, or security considerations innately relevant to taking screenshots. The description is too minimal for a complete understanding.
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 0% and the description directly explains none of the parameters. The `url` parameter is self-evident from context, but `width` is completely unexplained. The description fails to provide any additional meaning beyond what the schema already implies, forcing the agent to guess its purpose.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: capturing a full-page screenshot of any URL and producing a PNG. It is specific and unambiguous, and distinguishes itself from sibling tools (none of which are screenshot-related) by using a direct verb and resource.
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 does not provide explicit guidance on when to use this tool versus alternatives; it only includes a marketing comparison to paid APIs. There are no direct usage instructions, prerequisites, or context about typical use cases, so the agent is left without clear guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Plain-language request + table schema → ready-to-run .sql file with comments.
| Name | Required | Description | Default |
|---|---|---|---|
| schema | No | ||
| dialect | No | ||
| request | No |
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 mentions that it returns a .sql file with comments, but it doesn't disclose edge cases, limitations (e.g., complexity of queries, dialect support), or what happens if the request is ambiguous or the schema is incomplete.
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 one concise sentence that front-loads the purpose and output. It's efficient 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 complexity (SQL generation) and the lack of annotations and output schema, the description is somewhat minimal. It doesn't explain how the dialect parameter works, what inputs are expected (e.g., schema format), or any limitations. For a tool with this complexity, more detail would be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 3 parameters (schema, dialect, request) but 0% description coverage. The description mentions 'table schema' and 'plain-language request', which loosely maps to 'schema' and 'request', but it doesn't explain the 'dialect' parameter or what values it accepts. The description adds minimal value over 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 tool takes a plain-language request and table schema to produce a ready-to-run .sql file with comments. It distinguishes from siblings like transpile_sql (which converts between SQL dialects) and regex_test, but it doesn't explicitly name alternatives.
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 implies the use case of generating SQL from natural language and schema, but it doesn't provide explicit when-to-use or when-not-to-use guidance or mention alternatives like transpile_sql for converting SQL. The distinction from siblings is minimal.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Convert a time between time zones, or get current time in any zone. from/to = IANA zone (Asia/Shanghai), time = YYYY-MM-DDTHH:mm (default now).
| Name | Required | Description | Default |
|---|---|---|---|
| to | No | ||
| from | No | ||
| time | No |
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 states the function (conversion/current time) and parameter formats but does not disclose behavioral traits such as output format, error handling for invalid zones, or whether it requires network access. For a pure utility, this is minimally acceptable but could be more explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core action, and every word contributes value. It efficiently packs purpose, parameter semantics, and default behavior 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 and has no output schema or annotations. The description covers purpose and parameter basics, but it leaves ambiguity about what happens when 'from' or 'to' are omitted (e.g., if only 'to' is given, does it assume current time?). It also does not mention what the output looks like (e.g., formatted time string). For a simple tool, 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 0%; all three parameters are bare strings. The description adds critical meaning by specifying that 'from' and 'to' are IANA timezone identifiers (with example 'Asia/Shanghai') and that 'time' follows YYYY-MM-DDTHH:mm format with a default of 'now'. This significantly compensates for the schema's lack of details, though it does not clarify interactions among 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 converts time between time zones or gets current time in any zone, using a specific verb ('convert') and resource (time zones). It inherently distinguishes itself from siblings, which are unrelated to time utilities. The phrase 'from/to = IANA zone' adds intentional scoping.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by explaining what it does and gives examples of parameter formats, but it does not explicitly state when to use this tool over alternatives or exclude any scenarios. It provides enough context for a simple conversion tool, but lacks explicit when/when-not guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Convert a SQL statement from one dialect to another — mysql, postgres, sqlite, tsql, oracle, snowflake, bigquery, redshift, spark, hive, presto, trino, duckdb, clickhouse, databricks, doris, starrocks and more. Deterministic parser (sqlglot), not an LLM: the same input always produces the same output, and syntax errors come back with the exact line and column. Use it when migrating queries between databases or debugging dialect-specific syntax.
| Name | Required | Description | Default |
|---|---|---|---|
| sql | Yes | The SQL statement (or several, separated by semicolons). | |
| read | No | Source dialect, e.g. "mysql". Omit to auto-detect from generic SQL. | |
| write | Yes | Target dialect, e.g. "postgres", "bigquery", "doris". |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly/idempotent, but the description adds determinism ('same input always produces the same output') and exact error locations (line/column), going beyond structured metadata. This is valuable 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?
Three sentences, each valuable: purpose, determinism, and use case. Front-loaded and 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?
Description covers purpose, usage, determinism, and error behavior. Output schema covers return shape. For a conversion tool with rich annotations, it's sufficiently 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 covers all parameters at 100%, so baseline is 3. The description adds a useful list of supported dialects for read/write, aiding parameter selection, though it doesn't add much beyond that.
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?
Clearly states a specific verb ('Convert') and resource ('a SQL statement from one dialect to another') and lists many supported dialects. It distinguishes itself from sibling formatting tools by focusing on SQL dialect conversion.
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 migrating queries between databases or debugging dialect-specific syntax'). It doesn't mention exclusions or alternatives, but the sibling tools are unrelated, so the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Convert units: length, weight, temperature, area, volume, speed, data size. value + from + to (e.g. 100 km mi, 25 c f).
| Name | Required | Description | Default |
|---|---|---|---|
| to | No | ||
| from | No | ||
| value | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden, and it does a reasonable job by spelling out the invocation contract and giving concrete examples. It does not describe output formatting or invalid-unit behavior, but for a side-effect-free conversion tool this is not a critical omission.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single front-loaded sentence followed by an example, with no filler or redundant restating of the title. Every phrase adds either scope or usage detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple three-parameter converter with no output schema, the description is reasonably complete: it communicates scope, parameter order, and examples. It could be improved by listing accepted unit aliases or edge-case behavior, but the tool is simple enough that an agent can construct a valid call.
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 0%, so the description must compensate; it does by defining roles through the pattern 'value + from + to' and examples like '100 km mi, 25 c f'. This makes value, from, and to understandable despite the schema providing only raw types.
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 immediately states 'Convert units' and enumerates specific categories (length, weight, temperature, area, volume, speed, data size), giving a precise verb+resource scope. This also differentiates it from sibling timezone_convert by explicitly covering non-timezone unit types.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description makes it clear this should be used for unit conversions and shows the required input pattern 'value + from + to'. It does not explicitly mention alternatives or exclusions, but the domain is unambiguous and the sibling list reinforces that timezone conversions belong to a different tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Checks that text parses as JSON, and optionally that required keys are present with the right top-level types. Returns the specific violations, not just true/false. Checks required + types only — not full JSON Schema, and it says so rather than pretending. Use before feeding generated JSON into something that will fail on it.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The JSON to validate. | |
| schema | No | Optional JSON Schema (as JSON text) — required[] and properties[].type are checked. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the read-only/idempotent annotations, the description discloses important behavior: it returns specific violations rather than a boolean, and explicitly states the scope (required + types only) while disclaiming full JSON Schema support. This adds substantive context not captured by 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?
Four sentences, each with a distinct purpose: what it does, what it returns, its limitations, and when to use it. No wasted words; the information is front-loaded and efficiently delivered.
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 (2 params, one required) and has a full output schema, so the description does not need to explain return values. It covers the essential behavioral aspects (parsing, optional key/type checks, violation reporting, and scope limitations) adequately for an agent to use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already provides 100% coverage for both parameters, with descriptions for 'text' and 'schema' including details about required[] and properties[].type. The description adds only a mild restatement of the schema's functionality ('required keys are present with the right top-level types'), not significantly new meaning, so it meets the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Checks that text parses as JSON') and clearly states the resource (text) and optional key/type validation. It distinguishes itself from siblings like json_yaml by specifying it validates JSON and explicitly scopes to required keys and types, not full JSON Schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives an explicit use case ('Use before feeding generated JSON into something that will fail on it') and a clear limitation ('not full JSON Schema'), implying when not to use it. However, it does not name an alternative tool for full schema validation, so it falls short of the full when/when-not/alternatives guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Describe a task in plain language (any language) and get back exactly which tools on this server do it, with ready-to-run example calls — instead of reading the whole catalogue and guessing. Also returns multi-step recipes when a task needs several tools chained (invoices to a ledger, a bank statement reconciled, a messy CSV turned into a deliverable). Deterministic and free: it calls no model, costs nothing, and never runs out of quota. Call this FIRST when you are not sure what this server offers.
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | What you are trying to do, e.g. "reconcile a bank statement against my books" or "把一堆发票整理成能入账的表格" |
| 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. The description adds valuable behavioral traits not visible in annotations: deterministic, calls no model, costs nothing, and never runs out of quota. 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?
Four dense sentences, front-loaded with the core purpose in the first sentence and supported by recipes, cost/quota guarantees, and when-to-use guidance. Every sentence earns its place; no filler or repetition.
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 context (single required string parameter, rich annotations, and an output schema), the description is complete. It covers the tool's behavior, return value highlights, performance/cost characteristics, and usage context without leaving important 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 input schema already describes the 'task' parameter with examples, and schema coverage is 100%. The description adds the nuance 'plain language (any language)' and emphasizes that example calls are generated, providing modest additional meaning 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 tool's purpose with a specific verb+resource: describe a task and get back exactly which tools on the server do it, with ready-to-run example calls. It explicitly distinguishes itself from sibling tools by positioning itself as the meta-guide that prevents 'reading the whole catalogue and guessing.'
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 guidance: 'Call this FIRST when you are not sure what this server offers.' It also clarifies the intended use case (plain-language task discovery) and contrasts with the alternative of scanning the catalogue manually, which is sufficient for a meta-tool.
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.
json_format3 fields changedInput schema / properties / indentAdded value: +{
+ "type": "number"
+}Input schema / properties / jsonAdded value: +{}Input schema / properties / urlAdded value: +{}screenshot_url2 fields changedInput schema / properties / urlAdded value: +{}Input schema / properties / widthAdded value: +{
+ "type": "number"
+}sql_from_description3 fields changedInput schema / properties / dialectAdded value: +{}Input schema / properties / requestAdded value: +{}Input schema / properties / schemaAdded value: +{
+ "type": "string"
+}timezone_convert3 fields changedInput schema / properties / fromAdded value: +{
+ "type": "string"
+}Input schema / properties / timeAdded value: +{
+ "type": "string"
+}Input schema / properties / toAdded value: +{
+ "type": "string"
+}unit_convert3 fields changedInput schema / properties / fromAdded value: +{
+ "type": "string"
+}Input schema / properties / toAdded value: +{
+ "type": "string"
+}Input schema / properties / valueAdded value: +{
+ "type": "number"
+}screenshot_urlsql_from_descriptiondiff_textjson_formatjson_yamljwt_decodemake_qrredact_textregex_testtimezone_converttranspile_sqlunit_convertvalidate_jsonwhat_can_you_do13 micro-tools for agents: read pages, verify email, convert, validate, diff, extract. AI-operated.
Exact hashing, base64/hex/URL encoding, JWT decoding and UUIDs for AI agents. No auth required.
300+ deterministic data utilities for AI agents: validate, normalize, parse, match, redact.
500+ deterministic tools for AI agents: math, conversion, validation, hashing, encoding, date/time.
Add one secure layer between your agents and this server.
