rnv-color-mcp
Server Details
rnv-color-mcp, a hosted color-workflow MCP server (Python, Streamable HTTP).
Tools: mix (including Kubelka-Munk paint physics), convert between formats, generate harmonies, transform text case, and save/list/get named palettes. It resolves hex / CSS / custom brand color names and refuses unknown colors rather than guessing.
- 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 4.9/5 across 9 of 9 tools scored.
The color tools (difference, contrast, conversion, harmony, mixing, palette) are all clearly distinct and well-differentiated by their descriptions. The inclusion of transform_text is off-topic but clearly described as a text transformation tool, so it does not cause confusion with the color tools.
Most tool names follow a verb_noun pattern (generate_harmony, convert_color, mix_colors, etc.), but color_difference and contrast_check use noun_noun. This minor inconsistency prevents a perfect score, but overall the naming is predictable and readable.
With 9 tools, the server is well-scoped for its purpose. Each tool addresses a distinct color-related operation or palette management, and the count is within the optimal range for a focused MCP server.
The server covers core color operations (difference, contrast, conversion, harmony, mixing) and palette CRUD (create, read, list). Missing a delete palette tool and possibly advanced features like image extraction, but the essential workflows are present.
Available Tools
9 toolscolor_differenceAInspect
Perceptual difference (Delta-E) between two colors. color1 and color2 accept a hex, CSS name, RNV brand name, or saved-palette reference. method is 'ciede2000' (default, modern standard) or 'cie76'. A value near 1.0 is the threshold the eye can just notice; larger means more different. Returns the value and a plain-language interpretation. Read-only and deterministic, with no side effects. Use ciede2000 for accuracy and pick cie76 only to match a legacy system; to test whether text is legible on a background (not raw difference) use contrast_check instead.
| Name | Required | Description | Default |
|---|---|---|---|
| color1 | Yes | ||
| color2 | Yes | ||
| method | No | ciede2000 |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description explicitly declares 'Read-only and deterministic, with no side effects,' fully disclosing behavioral traits. Also describes the return value format (value and interpretation).
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?
Single paragraph packs essential information efficiently, but could be slightly more structured (e.g., bullet points for clarity). However, no superfluous content exists.
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, parameter meanings, usage guidance, behavioral traits, result interpretation, and sibling differentiation. With no annotations and zero schema coverage, the description provides complete context for correct tool usage.
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%, but description thoroughly explains allowed formats for color parameters (hex, CSS name, RNV brand, saved-palette reference) and method options (ciede2000 vs cie76), compensating fully for missing schema 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?
Explicitly states 'Perceptual difference (Delta-E) between two colors.' Clearly identifies the tool's function and distinguishes it from sibling tools like contrast_check.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance on when to use each method ('ciede2000 for accuracy, cie76 only to match legacy system') and directs to contrast_check for legibility testing. Also explains the significance of the value near 1.0.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
contrast_checkAInspect
WCAG contrast ratio between a foreground and background color, for accessibility. Both accept a hex, CSS name, RNV brand name, or saved-palette reference. Returns the ratio (1.0-21.0) plus pass/fail for AA and AAA at normal and large text sizes and for UI components. Read-only and deterministic, with no side effects. Use this for legibility and accessibility checks; to measure raw perceptual difference between two colors rather than readability use color_difference instead.
| Name | Required | Description | Default |
|---|---|---|---|
| background | Yes | ||
| foreground | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description fully discloses read-only and deterministic nature with no side effects. Also explains input format variety and output details (ratio plus pass/fail thresholds).
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 front-loaded with purpose, then covering inputs, outputs, and usage context. No filler; 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 the tool has 2 simple parameters and an output schema, the description covers all necessary aspects: input formats, behavior, output details, and usage context. 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?
Adds substantial meaning beyond the schema: both parameters accept hex, CSS names, RNV brand names, or palette references. Schema only says 'string', so this is critical context for correct usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool checks WCAG contrast ratio for accessibility, specifying verb 'check' and resource 'contrast ratio'. It distinguishes from sibling tool 'color_difference' by noting the difference between readability and perceptual difference.
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?
Explicit guidance on when to use (legibility/accessibility checks) and when not to use (raw perceptual difference), with direct mention of alternative sibling 'color_difference'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
convert_colorAInspect
Convert a color between formats. Input accepts a hex, CSS name, RNV brand name, or saved-palette reference. With to set to one of hex/rgb/hsv/hsl/lab, returns just that format; otherwise returns all of them. Read-only and deterministic, with no side effects. Use for format conversion of a single color; to blend several colors into one use mix_colors, and to compare two colors use color_difference.
| Name | Required | Description | Default |
|---|---|---|---|
| to | No | ||
| color | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully covers behavior: declares 'Read-only and deterministic, with no side effects.' Also explains input flexibility and conditional output. No contradictions.
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 earning its place. Front-loaded with purpose, then input specs, output behavior, side effects, and usage alternatives. No 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 output schema exists, the description covers input, output semantics, behavioral traits, and alternatives. Complete for a conversion 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 has no descriptions (0% coverage). The description compensates by explaining that 'color' accepts hex, CSS name, RNV brand name, or saved-palette reference, and that 'to' can be one of hex/rgb/hsv/hsl/lab. Adds significant 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 verb 'Convert' and the resource 'a color between formats.' It specifies input types and output behavior, and distinguishes from sibling tools like mix_colors and color_difference.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says when to use this tool ('for format conversion of a single color') and when not ('to blend several colors into one use mix_colors, and to compare two colors use color_difference'). No ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
generate_harmonyAInspect
Generate a color harmony from a base color. base accepts a hex, CSS name, RNV brand name, or saved-palette reference (e.g. 'Spring line:2'). scheme is one of: complementary, analogous, triadic, split-complementary, tetradic (a.k.a. square), monochromatic, compound. Returns a list of hex colors. Read-only and deterministic: it derives the colors from the base and stores nothing, so it has no side effects and is safe to call repeatedly. Use to expand one base color into a related set; to blend existing colors into a single color use mix_colors, and to persist a set you like use save_palette.
| Name | Required | Description | Default |
|---|---|---|---|
| base | Yes | ||
| scheme | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description explicitly states the tool is read-only, deterministic, has no side effects, and is safe to call repeatedly. Since no annotations are provided, this full disclosure is essential and well-executed.
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 (4 sentences), front-loaded with the core purpose, then covers parameter details and usage guidelines. Every sentence adds value, with no redundancy or extraneous 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 the tool's moderate complexity and the presence of an output schema, the description covers all necessary aspects: what it does, how to use it, what the inputs mean, behavioral guarantees, and alternatives. 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 0% description coverage with no enums, but the description fully compensates by detailing that base accepts hex, CSS names, brand names, or saved-palette references, and scheme is one of seven specific types. This adds significant meaning beyond the raw 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 it generates a color harmony from a base color, lists the accepted input formats and scheme options, and explicitly distinguishes from sibling tools by naming mix_colors and save_palette. The purpose 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 provides explicit guidance on when to use this tool ('expand one base color into a related set') and when to use alternatives ('blend existing colors into a single color use mix_colors', 'persist a set you like use save_palette'). This helps the agent choose correctly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_paletteAInspect
Retrieve one saved palette by name, returning its colors and metadata. Returns null if no palette by that name exists. Read-only; no side effects. Use when you already know the palette name; to list available names first use list_palettes, and to create or update a palette use save_palette.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description explicitly states it is read-only with no side effects, and that it returns null if the palette does not exist, fully covering behavioral traits beyond 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 concise sentences front-load key information 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 simple retrieval tool with one parameter and a specified output (colors, metadata, null), the description provides complete guidance.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Only one parameter 'name' exists with no schema description, but the description explains its role as the palette identifier. The tool's specificity compensates for schema gaps.
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 states the tool retrieves one saved palette by name, returning its colors and metadata, and explicitly names sibling tools for alternative actions.
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 states when to use (know the palette name) and when to use alternative tools (list_palettes for names, save_palette for create/update).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_palettesAInspect
List every saved palette as name + colors. Read-only; no side effects. Use to discover what palettes exist or to find a name before calling get_palette; to fetch one palette's full detail use get_palette, and to create or overwrite one use save_palette.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Declares 'Read-only; no side effects' which covers the main behavioral concern. No pagination info needed given empty input schema. Slight gap: no mention of authentication or rate limits, but acceptable for a simple list 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?
Two sentences, concise, front-loaded with purpose and read-only status. 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 no parameters and presence of output schema, the description fully covers purpose, usage, and sibling differentiation. No further details needed.
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?
No parameters exist; schema coverage is 100%. Baseline 4 applies as description does not need to add parameter 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?
Clearly states the tool lists every saved palette with name and colors. Distinguishes from siblings get_palette (fetch one) and save_palette (create/overwrite).
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 describes when to use (discover palettes, find name before get_palette) and when not (use siblings for fetching one or creating/overwriting).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
mix_colorsAInspect
Blend up to 12 colors into one. Each color may be a hex (#d2bc93), a CSS name (red), an RNV brand name (brand gold, near-black), or a saved-palette reference (Spring line, or 'Spring line:2' for its 2nd swatch). Optional integer weights bias the blend (defaults to equal). mode selects the model: rgb/hsv/lab are digital blends (lab is perceptual and the default, best for on-screen color); paint mixes pigments via Kubelka-Munk physics (colors darken like real paint, use it for physical-media matching); ryb is the artist's color wheel; cmy is subtractive like printer inks. Returns hex and rgb. Read-only and deterministic: it computes a result and stores nothing, so it is safe to call repeatedly with no side effects. Use to combine multiple colors into a single blend; to convert one color between formats use convert_color, and to measure how far apart two colors are use color_difference.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | lab | |
| colors | Yes | ||
| weights | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
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 states 'Read-only and deterministic: it computes a result and stores nothing, so it is safe to call repeatedly with no side effects.' This fully discloses behavioral traits without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single paragraph but is well-organized and front-loaded. Every sentence adds value, though slightly more structured formatting (e.g., line breaks) could improve readability slightly.
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 complexity of color blending with multiple modes, the description covers all aspects: input formats, parameters, behavior, modes, and safety. Output schema exists, so return values are not required, but it still mentions returning hex and rgb.
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?
With 0% schema coverage, description compensates fully. It explains each parameter: colors (array with allowed formats), weights (optional integer array, default equal), mode (detailed explanation of each model, including defaults). This adds significant 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: 'Blend up to 12 colors into one.' It specifies input formats (hex, CSS names, brand names, palette references) and output (hex and rgb). It distinguishes from sibling tools like convert_color and color_difference by naming them explicitly.
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?
Explicit guidance: use for combining multiple colors into a single blend; for converting one color use convert_color; for measuring distance use color_difference. This directly tells the agent when to use this tool vs. alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
save_paletteAInspect
Persist a named color palette for later retrieval with get_palette or list_palettes. colors is a list of hex values; optional notes are stored as the palette's description. Author is recorded as RNVizion. This WRITES to the palette store and is the only tool here that does. Reusing an existing name overwrites that palette: save and update are the same call (an upsert), there is no separate update operation. Returns a durable flag: true if the palette reached durable storage (the HF Dataset) and will survive a restart, false if it saved to the local working copy only (which is lost on rebuild, e.g. when the Space HF_TOKEN is missing or lacks write scope). Use when the user wants to keep a set of colors under a name for reuse across sessions, such as a brand or launch palette; to read a palette back use get_palette, and to see what already exists use list_palettes. The saved name can then be passed to mix_colors, convert_color, and generate_harmony as a palette reference.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Unique key the palette is stored under. Reusing an existing name overwrites that palette (upsert). Can be referenced later by other tools as 'name:index', e.g. 'Spring line:2'. | |
| notes | No | Optional human-readable description stored as the palette's notes. | |
| colors | Yes | Ordered list of hex colors, each '#RRGGBB' (e.g. '#d2bc93'). Order is preserved; at least one required. |
Output Schema
| Name | Required | Description |
|---|---|---|
| name | Yes | Name the palette was stored under. |
| notes | Yes | Description stored on the palette; empty if none given. |
| colors | Yes | The hex colors saved, in order. |
| durable | Yes | True if the palette was written through to the durable HF Dataset and will survive a Space rebuild; False if it saved to the local working copy only (e.g. the Space HF_TOKEN is missing or lacks write scope), meaning it will be lost on the next restart. |
| color_count | Yes | Number of colors in the saved palette. |
| overwritten | Yes | True if a palette with this name already existed and was replaced; False if newly created. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description fully discloses behavioral traits: it WRITES, it is an upsert (reusing name overwrites), there is no separate update operation, author is recorded as RNVizion, and returns a `durable` flag with explanation of its meaning. All relevant behaviors are 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 appropriately sized for the tool's complexity. It is well-structured: first sentence gives purpose, then parameter details, then behavioral traits, then usage guidance. Every sentence adds distinct value with 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?
Given the 3 parameters, output schema exists, and sibling tools, the description covers all aspects: purpose, parameter semantics, behavior (upsert, write nature), return value (durable flag), usage guidance, and cross-references. There are 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, so baseline is 3. The description adds value beyond schema by explaining the upsert behavior for the name parameter, clarifying that colors is a list of hex values, and noting that notes are stored as description. The 'name:index' reference format is also helpful.
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: 'Persist a named color palette for later retrieval'. It uses specific verbs ('persist', 'writes') and a specific resource ('palette store'). It distinguishes from siblings by noting this is the only tool that writes, listing get_palette and list_palettes as 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?
Explicitly states when to use: 'Use when the user wants to keep a set of colors under a name for reuse across sessions'. Also provides specific when-not-to-use guidance: 'to read a palette back use get_palette, and to see what already exists use list_palettes'. Further explains how saved names can be referenced by other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
transform_textAInspect
Apply an exact, deterministic text transformation. operation is one of: UPPERCASE, lowercase, 'Title Case', 'Sentence case', camelCase, PascalCase, snake_case, CONSTANT_CASE, kebab-case, dot.case, 'iNVERTED cASE'. Read-only and deterministic: it returns the transformed string and changes nothing, safe to call repeatedly. Use whenever exact, reproducible case formatting matters rather than rewriting the text by hand or guessing the casing.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| operation | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses that the tool is read-only, deterministic, and safe to call repeatedly. It explicitly states it changes nothing, covering behavioral traits comprehensively.
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, each serving a distinct purpose: listing operations and providing usage/behavioral notes. It is front-loaded with the core action and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given low complexity, existing output schema, and no close sibling tools, the description covers purpose, parameter values, behavior, and usage guidance comprehensively.
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?
Despite 0% schema description coverage, the description enumerates all valid operations for the 'operation' parameter and implies 'text' is the input string. This adds essential meaning beyond the minimal 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 it applies deterministic text transformations, lists all supported cases (UPPERCASE, lowercase, etc.), and distinguishes itself from unrelated sibling tools (color tools). It uses specific verbs and resources.
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 advises using the tool whenever exact, reproducible case formatting is needed, providing clear context. It does not explicitly mention when not to use it, but there are no closely related siblings, so 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.
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.
Discussions
No comments yet. Be the first to start the discussion!