colorapi
Server Details
Color API MCP — wraps thecolorapi.com (free, no auth)
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- pipeworx-io/mcp-colorapi
- GitHub Stars
- 0
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Usage analytics
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Tool Definition Quality
Average 3.8/5 across 8 of 8 tools scored. Lowest: 2.9/5.
The tool set has clear distinctions between color-related tools (convert_color, generate_scheme, identify_color) and memory/utility tools (remember, recall, forget, discover_tools, ask_pipeworx), but there is overlap in the memory tools where recall and forget both interact with stored memories, and ask_pipeworx could be confused with discover_tools as both help find or use tools, though their descriptions clarify different purposes.
Most tools follow a consistent verb-based naming pattern (e.g., convert_color, generate_scheme, identify_color, discover_tools, forget, recall, remember), with ask_pipeworx being a minor deviation due to its brand-specific name. Overall, the naming is readable and predictable, with only one outlier in an otherwise uniform set.
With 8 tools, the count is reasonable for a server that combines color processing with memory and utility functions. It's slightly broad in scope but manageable, as each tool serves a distinct role without being excessive or too sparse for the intended functionality.
For color-related operations, the tools cover conversion, identification, and scheme generation, which is fairly complete for basic color tasks. However, there are gaps such as missing color manipulation tools (e.g., adjust brightness or saturation) and the memory tools are basic CRUD but lack advanced features like search or bulk operations. The utility tools are present but not deeply integrated with the color domain.
Available Tools
8 toolsask_pipeworxAInspect
Ask a question in plain English and get an answer from the best available data source. Pipeworx picks the right tool, fills the arguments, and returns the result. No need to browse tools or learn schemas — just describe what you need. Examples: "What is the US trade deficit with China?", "Look up adverse events for ozempic", "Get Apple's latest 10-K filing".
| Name | Required | Description | Default |
|---|---|---|---|
| question | Yes | Your question or request in natural language |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it picks the right tool, fills arguments automatically, and returns results. However, it doesn't mention limitations like rate limits, response formats, or error conditions that would be helpful for complete 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 perfectly concise and well-structured: it starts with the core functionality, explains the value proposition, provides clear examples, and every sentence earns its place. The information is front-loaded 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 (natural language processing with automatic tool selection) and lack of annotations/output schema, the description does a good job explaining what the tool does. However, it could benefit from mentioning what types of results to expect or any limitations, making it slightly incomplete for full contextual 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?
The input schema has 100% description coverage, so the schema already documents the single 'question' parameter adequately. The description adds minimal value beyond the schema by reinforcing that questions should be in 'plain English' or 'natural language,' but doesn't provide additional syntax or format details.
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: to answer questions in plain English by selecting the best data source and returning results. It provides specific examples that illustrate the verb+resource combination and distinguishes it from sibling tools by emphasizing natural language processing rather than structured operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool: for asking questions in plain English when you don't want to browse tools or learn schemas. It provides clear examples of appropriate use cases and implicitly distinguishes it from sibling tools that require more structured inputs.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
convert_colorAInspect
Convert between color formats: RGB to hex, HSL, HSV, CMYK. Returns the closest named color for the input values.
| Name | Required | Description | Default |
|---|---|---|---|
| b | Yes | Blue channel (0-255). | |
| g | Yes | Green channel (0-255). | |
| r | Yes | Red channel (0-255). |
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 mentions the conversion outputs (hex, HSL, HSV, CMYK, color name) but does not disclose behavioral traits such as error handling for invalid inputs, performance characteristics, or any limitations (e.g., precision of conversions). This leaves gaps in understanding how the tool behaves beyond its basic 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?
The description is a single, efficient sentence that front-loads the core purpose (conversion) and lists all outputs without unnecessary details. Every word contributes to understanding the tool's function, making it appropriately sized 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?
Given the tool's moderate complexity (conversion with multiple outputs), no annotations, and no output schema, the description is somewhat complete but lacks details on return values, error cases, or behavioral context. It covers what the tool does but not how it operates or what results to expect, leaving room for improvement in 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 description coverage is 100%, with clear documentation of r, g, b parameters as RGB channels with range 0-255. The description adds no additional parameter semantics beyond what the schema provides, such as format details or constraints. Baseline 3 is appropriate since the schema adequately covers parameter information.
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 specific action ('convert an RGB color') and the comprehensive outcome ('to all other color formats... and get its closest color name'). It distinguishes from sibling tools like 'generate_scheme' and 'identify_color' by focusing on format conversion rather than scheme generation or color identification.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when RGB input is available and conversion to multiple formats is needed, but it does not explicitly state when to use this tool versus alternatives like 'generate_scheme' or 'identify_color'. No exclusions or prerequisites are mentioned, leaving usage context somewhat vague.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
discover_toolsAInspect
Search the Pipeworx tool catalog by describing what you need. Returns the most relevant tools with names and descriptions. Call this FIRST when you have 500+ tools available and need to find the right ones for your task.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of tools to return (default 20, max 50) | |
| query | Yes | Natural language description of what you want to do (e.g., "analyze housing market trends", "look up FDA drug approvals", "find trade data between countries") |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it's a search operation that returns relevant tools with names and descriptions, and specifies it should be called first in large tool catalogs. However, it lacks details on rate limits, error handling, or authentication needs, which would be beneficial for a discovery tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by usage guidance, all in two concise sentences. Every sentence adds value: the first explains what the tool does, and the second provides critical context on when to use it, 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 moderate complexity (search functionality with 2 parameters), no annotations, and no output schema, the description is largely complete. It covers purpose, usage context, and behavioral aspects, but could improve by hinting at return format or error cases, though not strictly required since output schema is absent.
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 description coverage is 100%, so the schema already fully documents the parameters (query and limit). The description adds minimal value beyond the schema by mentioning 'describing what you need' for the query, but does not provide additional syntax, format, or usage context for parameters. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Search the Pipeworx tool catalog') and resources ('tool catalog'), and explicitly distinguishes it from sibling tools by emphasizing its role in discovering tools among 500+ options, unlike the color-related siblings (convert_color, generate_scheme, identify_color).
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 this tool ('Call this FIRST when you have 500+ tools available and need to find the right ones for your task') and includes a clear alternative scenario (implicitly suggesting not to use it for simple tasks with fewer tools). This directly addresses when and why to choose this tool over others.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
forgetCInspect
Delete a stored memory by key.
| Name | Required | Description | Default |
|---|---|---|---|
| key | Yes | Memory key to delete |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states the basic action ('Delete'). It doesn't disclose behavioral traits like whether deletion is permanent/reversible, permission requirements, error handling for non-existent keys, or side effects. This is inadequate for a destructive operation with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's front-loaded with the core action and resource, making it easy to parse quickly. Every word earns its place 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?
For a destructive tool with no annotations and no output schema, the description is incomplete. It lacks crucial context: what 'stored memory' means in this system, confirmation of deletion, error responses, or impact on related data. The agent must guess these behavioral aspects.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the parameter 'key' fully documented in the schema. The description adds no additional meaning beyond implying the key identifies a memory, which the schema already covers. Baseline 3 is appropriate when the schema does all the work.
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 ('Delete') and resource ('a stored memory by key'), making the purpose immediately understandable. It distinguishes from sibling 'recall' (which retrieves) and 'remember' (which stores), but doesn't explicitly contrast with other siblings like 'convert_color' or 'identify_color', keeping it at 4 rather than 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing memory key), exclusions, or compare it to other deletion-related tools (none in siblings), leaving the agent to infer usage from context alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
generate_schemeCInspect
Generate harmonious color palettes from a seed hex color (e.g., "#3498DB"). Returns complementary, analogous, triadic, or monochromatic schemes with hex codes.
| Name | Required | Description | Default |
|---|---|---|---|
| hex | Yes | Seed hex color value without the # prefix (e.g. "FF5733"). | |
| mode | No | Color scheme mode. One of: monochrome, analogic, complement, triad, quad. Defaults to "monochrome". | |
| count | No | Number of colors to return (1-10, default 5). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool returns 'a set of harmonious colors' but doesn't describe the return format (e.g., array of hex values), potential errors (e.g., invalid hex input), or any side effects. For a tool with zero annotation coverage, this leaves significant behavioral gaps, though it does mention the output is based on the chosen mode.
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, well-structured sentence that efficiently conveys the core functionality: generating a color scheme from a seed hex with mode-based harmony. It's front-loaded with the main purpose and avoids unnecessary details, making it highly concise with zero waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no annotations and no output schema, the description is incomplete. It doesn't explain what the returned 'set of harmonious colors' looks like (e.g., format, structure), potential constraints, or error handling. For a tool with 3 parameters and no structured output information, the description should provide more context to be fully helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all three parameters (hex, mode, count) with details like format, enums, and defaults. The description adds minimal value beyond the schema by implying the hex is a seed and mode influences harmony, but doesn't provide additional syntax or usage context. This meets the baseline of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate a color scheme from a seed hex color' specifies the verb (generate) and resource (color scheme). It distinguishes from sibling tools like 'convert_color' and 'identify_color' by focusing on scheme generation rather than conversion or identification. However, it doesn't explicitly differentiate from potential overlapping functionality, keeping it at a 4 rather than a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'convert_color' or 'identify_color'. It mentions 'based on the chosen mode' but doesn't explain when to choose different modes or what contexts suit this tool. There are no explicit when/when-not statements or prerequisites, resulting in minimal usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
identify_colorAInspect
Identify a color by hex code (e.g., "#FF5733"). Returns color name, RGB/HSL/HSV/CMYK values, and WCAG contrast ratios for accessibility.
| Name | Required | Description | Default |
|---|---|---|---|
| hex | Yes | Hex color value without the # prefix (e.g. "FF5733"). |
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 of behavioral disclosure. It mentions the return values (color name, format representations, contrast info), which is helpful, but it doesn't describe error handling (e.g., invalid hex values), performance characteristics, rate limits, or authentication needs. For a tool with zero annotation coverage, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded, consisting of just two sentences that directly state the tool's purpose and output. Every sentence earns its place by providing essential information without redundancy or fluff, making it highly efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one parameter, no annotations, no output schema), the description is somewhat complete but has gaps. It explains what the tool does and what it returns, which is adequate for basic use. However, without annotations or an output schema, it lacks details on error cases, behavioral traits, and exact return structure, making it minimally viable but not fully comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents the 'hex' parameter thoroughly. The description adds marginal value by reinforcing the purpose ('identify a color by its hex value'), but it doesn't provide additional syntax, format details, or constraints beyond what the schema specifies. With only one parameter and high schema coverage, a baseline of 3 is appropriate, but the description's clarity elevates it slightly.
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 specific action ('identify a color by its hex value') and the resource (color information). It distinguishes from sibling tools like 'convert_color' (which likely transforms between formats) and 'generate_scheme' (which likely creates color palettes) by focusing on identification and comprehensive representation extraction rather than conversion or generation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you have a hex value and need detailed color information, but it doesn't explicitly state when to use this tool versus alternatives like 'convert_color' or 'generate_scheme'. There's no guidance on prerequisites, exclusions, or specific scenarios where this tool is preferred over others, leaving usage context somewhat ambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
recallAInspect
Retrieve a previously stored memory by key, or list all stored memories (omit key). Use this to retrieve context you saved earlier in the session or in previous sessions.
| Name | Required | Description | Default |
|---|---|---|---|
| key | No | Memory key to retrieve (omit to list all keys) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the dual behavior (retrieve by key vs. list all) and persistence across sessions, which is valuable. However, it doesn't address potential edge cases like what happens with invalid keys, whether there are size/rate limits, or authentication requirements. For a tool with no annotations, this leaves some behavioral aspects unspecified.
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 perfectly concise with two sentences that each serve distinct purposes. The first sentence explains the dual functionality and parameter usage. The second sentence provides the usage context. There's no wasted text, repetition, or unnecessary elaboration. The information is front-loaded with the core functionality stated immediately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with good schema coverage but no annotations or output schema, the description does well. It explains the tool's purpose, usage guidelines, and behavioral context. The main gap is the lack of output information - we don't know what format memories are returned in or what the list output looks like. However, given the tool's relative simplicity, the description provides most of what an agent needs 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 has 100% description coverage, so the baseline is 3. The description adds meaningful context by explaining the semantic implication of omitting the key parameter: 'omit to list all keys' (not just 'omit key' as in the schema). This clarifies that omitting the parameter changes the tool's behavior from retrieval to listing, which is valuable guidance beyond the schema's technical specification.
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 specific verbs ('retrieve', 'list') and resources ('previously stored memory', 'all stored memories'). It distinguishes from sibling tools like 'remember' (store) and 'forget' (delete) by focusing on retrieval operations. The phrase 'by key' adds specificity about the retrieval mechanism.
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 this tool: 'to retrieve context you saved earlier in the session or in previous sessions.' It also gives clear parameter-based usage rules: 'Retrieve a previously stored memory by key, or list all stored memories (omit key).' This tells the agent exactly when to use each mode of operation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
rememberAInspect
Store a key-value pair in your session memory. Use this to save intermediate findings, user preferences, or context across tool calls. Authenticated users get persistent memory; anonymous sessions last 24 hours.
| Name | Required | Description | Default |
|---|---|---|---|
| key | Yes | Memory key (e.g., "subject_property", "target_ticker", "user_preference") | |
| value | Yes | Value to store (any text — findings, addresses, preferences, notes) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the persistence characteristics (persistent for authenticated users, 24-hour lifespan for anonymous sessions) and the cross-tool context capability ('across tool calls'). However, it doesn't mention potential limitations like storage capacity, key constraints, or error conditions.
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 perfectly concise and well-structured: two sentences that each earn their place. The first sentence states the core functionality and usage context, while the second provides crucial behavioral context about persistence. There's zero wasted text, and important information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (storage with persistence rules), no annotations, and no output schema, the description does a good job covering key aspects: purpose, usage context, and persistence behavior. However, it doesn't mention what happens on success/failure or return values, which would be helpful since there's no output schema. The description compensates well but has minor gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents both parameters. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain key naming conventions or value formatting beyond the schema's examples). The baseline score of 3 is appropriate when the schema does the heavy lifting.
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 specific verbs ('store a key-value pair') and resource ('in your session memory'), distinguishing it from sibling tools like 'forget' (likely for deletion) and 'recall' (likely for retrieval). It explicitly mentions what gets stored ('intermediate findings, user preferences, or context across tool calls'), making the purpose unambiguous and differentiated.
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 this tool ('to save intermediate findings, user preferences, or context across tool calls') and includes important contextual distinctions ('Authenticated users get persistent memory; anonymous sessions last 24 hours'), which helps the agent decide when this tool is appropriate versus alternatives like 'recall' for retrieval.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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