Superseded listing, not a dead server. This entry was imported from an early Glama registration under com.moltlinestudio.mcp; the same server is listed under its official MCP Registry name — use com.moltlinestudio/research. Endpoint unchanged: https://mcp.moltlinestudio.com/research — still live, still free on the free tier. Only this duplicate entry is deprecated.
7 research and study persona products plus computed research mechanics over MCP: format_citation outputs APA 7 / MLA 9 / Chicago from raw fields, stats_describe computes full descriptive statistics, sample_size and confidence_interval handle survey math honestly, and get_free_skill loads any product's complete free gateway skill.
Compute a confidence interval for a mean (normal approximation). FREE.
Typical input {"mean": 72.4, "std_dev": 8.1, "n": 64, "confidence_pct": 95} returns {"mean": 72.4, "margin_of_error": 1.9845, "interval": [70.4155, 74.3845], "note": "..."}.
Use on data already collected, for a mean. Normal approximation, so it is unreliable on very small or heavily skewed samples. Not for proportions or two-group comparisons - the data server's ab_test compares two proportions. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "confidence 90/95/99, n>=2, std_dev>=0"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| n | Yes | Sample size; at least 2. | |
| mean | Yes | Sample mean. | |
| std_dev | Yes | Sample standard deviation; 0 or greater. | |
| confidence_pct | No | Confidence level; must be 90, 95, or 99. Default 95. |
| 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. The description reinforces these ('Every call is read-only and idempotent') and crucially adds error handling behavior: 'never raises a protocol error — it returns {"error": ...}' with a concrete example. This goes beyond annotations and provides essential safety knowledge.
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 dense but not wasteful: one sentence for purpose, one example block, one usage guideline, one error note, and one safety reassurance. Every sentence earns its place, and the structure is front-loaded with the core action.
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, the description covers the full picture: purpose, assumptions, alternative tool, error behavior, idempotency, and a worked example. The presence of an output schema reduces the need to detail return values, and the description compensates fully for any remaining 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 describes each parameter. The description adds value with a concrete example ('mean:72.4, std_dev:8.1, n:64, confidence_pct:95') that shows how inputs combine to produce outputs, plus error constraints ('confidence 90/95/99, n>=2, std_dev>=0') that reinforce schema rules. This extra context merits a 4 rather than the baseline 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 clearly states 'Compute a confidence interval for a mean (normal approximation)', specifying the exact verb and resource. The example input and output further clarify what the tool does, and the sibling tools (e.g., sample_size, stats_describe, ab_test) are distinct, so no confusion arises.
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 on data already collected, for a mean' and 'Not for proportions or two-group comparisons - the data server's ab_test compares two proportions.' It also warns about unreliability on very small or heavily skewed samples, giving clear when-to-use and when-not-to-use context with an alternative tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Format a citation in APA 7, MLA 9, or Chicago author-date style. FREE.
Typical input {"style": "apa", "authors": ["Curie, Marie"], "year": 1911, "title": "Radium and radioactivity", "container": "Century Magazine"} returns {"style": "apa", "citation": "Curie, M. (1911). Radium and radioactivity. Century Magazine.", "note": "..."}.
Use when the source details are already known and only the formatting is missing. Not for finding or verifying a source. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "style must be apa, mla, or chicago"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | Optional URL of the source. | |
| year | Yes | Publication year, e.g. 2024. | |
| style | Yes | Citation style: "apa", "mla", or "chicago" (case-insensitive). | |
| title | Yes | Title of the work being cited. | |
| authors | Yes | Authors as "Last, First" strings in source order, at least one, e.g. ["Curie, Marie"]. | |
| accessed | No | Optional access date for MLA web sources, e.g. "12 Aug. 2026". | |
| container | No | Optional journal, book, or site name. | |
| publisher | No | Optional publisher name. |
| 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. The description adds significant context by clarifying error handling in detail: 'on invalid, missing, or malformed input this tool never raises a protocol error.' This perfectly complements the 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?
The description is well-structured and front-loaded with the most critical information (the style, the free usage). The example is invaluable. The only minor issue is that the error handling section, while informative, could be slightly more concise without losing clarity.
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?
Despite the presence of an output schema, the description provides key return-value context by showing the exact structure of a successful response and, more importantly, the structure of all error responses. For a tool with 8 parameters (4 required), this is comprehensive and leaves no ambiguity about the tool's behavior.
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 at 100%, with all 8 parameters clearly described in the input schema. The description adds value by providing a concrete example showing how the parameters map to a citation string, highlighting that 'authors' expects 'Last, First' strings and that many parameters are optional with sensible defaults. This goes well beyond what the bare schema 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 clearly states the tool formats citations in APA 7, MLA 9, or Chicago style. It gives a detailed JSON example and contrasts with what the tool does not do (finding or verifying a source). This level of specificity effectively distinguishes it from siblings like 'sample_size' or 'stats_describe'.
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 the tool: 'when the source details are already known and only the formatting is missing.' It also clarifies what not to use it for: 'Not for finding or verifying a source.' While no explicit alternatives from the sibling list are named, this guidance is still quite strong.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Load a product's free gateway skill with its complete instructions. FREE.
Typical input {"slug": "thesis-advisor"} returns {"slug": ..., "skill": "", "instructions": ""}.
Returns exactly one skill - the product's free gateway skill - chosen automatically from the slug, with no plan required. Use when the caller wants usable instructions immediately. Not for the product's other skills: those are named and need get_full_skill with a skill_name, which requires a paid plan. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "unknown slug ''. Use list_products."}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Product slug exactly as returned by list_products, e.g. "thesis-advisor". |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark readOnlyHint and idempotentHint as true. The description adds value by detailing error handling (returns error object with fix suggestions), the automatic skill selection from slug, and that no plan is required—all beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-organized with a purpose statement, example, and key details. It is slightly lengthy but every sentence contributes meaning; 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 simple one-parameter tool, the existence of an output schema, and thorough coverage of usage, errors, and side effects, the description is complete. It does not omit necessary information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single slug parameter. The description adds context: slug format ('exactly as returned by list_products'), an example, and behavior for invalid input (returns error). This exceeds the schema description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool loads a product's free gateway skill with complete instructions. It specifies the return structure and distinguishes itself from get_full_skill, which requires a skill_name and a paid plan.
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: 'Use when the caller wants usable instructions immediately.' Contrasts with get_full_skill for other skills, and notes the tool is read-only and idempotent, implying safe retry.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Load one product in full: its persona plus every paid skill. PREMIUM (license).
Typical input {"slug": "thesis-advisor"} returns {"slug": ..., "name": ..., "persona": "", "skills": [{"name": ..., "instructions": ...}, ...], "free_skill": {...}}.
Returns persona plus every skill for one product. Use when the caller wants the whole product. Not for a single skill (get_full_skill) and not for a free look, which list_products and get_free_skill provide with no plan. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "unknown slug ''"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Product slug exactly as returned by list_products, e.g. "thesis-advisor". |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint, idempotentHint), the description adds 'Every call is read-only and idempotent' and details error handling: 'never raises a protocol error — it returns an error object'. This fully equips the agent with behavioral expectations.
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?
Well-structured and front-loaded: first sentence states purpose, then example, usage guidance, error handling, and safety. Some redundancy exists (purpose repeated in later sentence), but overall efficient. A slightly tighter version could earn 5.
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 a single parameter, rich annotations, an output schema, and multiple sibling tools, the description covers all needed context: scope, boundaries, error model, safety guarantees, and differentiation. 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 the slug parameter described. The description adds value with a typical input example, clarifies slug source ('exactly as returned by list_products'), and explains error behavior for invalid slugs, going well 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 states 'Load one product in full: its persona plus every paid skill' with a clear verb and resource. It immediately distinguishes itself from siblings like get_full_skill and list_products, making its unique purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says when to use ('caller wants the whole product') and when not to, naming alternatives ('get_full_skill for a single skill', 'list_products and get_free_skill for a free look'). Also explains error retry behavior, providing complete guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Load one paid skill's complete instructions from a product. PREMIUM (license).
Typical input {"slug": "thesis-advisor", "skill_name": "Outline Builder"} returns {"slug": ..., "skill": ..., "instructions": ""}.
Returns one named skill, selected by skill_name. Use when the caller wants one specific paid skill. Not for the free gateway skill, which get_free_skill returns with no plan, and not for every skill at once (get_full_product). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "unknown slug ''"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Product slug exactly as returned by list_products. | |
| skill_name | Yes | Exact skill name as listed in that product's "skills" array from list_products. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds error handling behavior beyond annotations: 'this tool never raises a protocol error — it returns {"error": ...}'. It also confirms idempotency and read-only nature, which aligns with annotations. No contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise—three short paragraphs covering purpose, example, usage guidelines, and error handling. Every sentence is necessary and well-structured, with no fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers purpose, usage, parameters, error handling, and retry safety. The output schema likely handles return structure details, so the description is complete for the tool's complexity. An agent has all necessary information to select and invoke the tool 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 already provides 100% coverage with clear descriptions for both parameters. The description adds value with a typical input example and clarifies that skill_name selects one named skill. This justifies moving above 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 clearly states the tool loads one paid skill's complete instructions from a product. It provides a concrete example and explicitly distinguishes from sibling tools get_free_skill and get_full_product, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use: 'Use when the caller wants one specific paid skill.' It also tells when not to use: 'Not for the free gateway skill... and not for every skill at once.' This provides clear guidance on alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List every product in the Research Desk line with its included skills. FREE.
Takes no arguments. Returns a list of 7 product objects, each {"slug": "thesis-advisor", "name": ..., "tagline": ..., "skills": ["Skill A", ...], "free_skill": "Gateway Skill Name"}. Use the returned slug values with get_free_skill, get_full_product, or get_full_skill.
Returns metadata only - no persona text and no skill instructions. Use when the caller wants to see what this server covers. Not for keyword search across the whole 138-product catalog, which the catalog server's search_catalog does, and not for instructions the caller can act on (get_free_skill). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Reinforces annotations (read-only, idempotent) and adds critical behavioral details: return format structure, metadata-only nature, no persona/skill instructions, and the error handling behavior (returns error object, never protocol error).
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?
Well-structured: purpose first, then return format, then usage guidelines, then error handling. Every sentence adds value. No redundancy or 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 zero parameters, rich annotations, and presence of an output schema, the description fully covers the tool's behavior, return structure, error handling, and relationship to sibling tools. 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?
No parameters exist in the input schema (coverage 100%). Baseline 4 is appropriate; the description correctly states 'Takes no arguments' and adds no further 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?
Clearly states the verb 'list', resource 'products', and scope 'Research Desk line'. Explicitly distinguishes from sibling tools by noting it returns metadata only and that the returned slugs are used with other tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit when-to-use ('when the caller wants to see what this server covers') and when-not-to-use ('not for keyword search...catalog server's search_catalog', 'not for instructions...get_free_skill'). Names alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Calculate the survey sample size needed for a confidence level and margin. FREE.
Uses maximum variance (p=0.5) with a finite-population correction when population is given. Typical input {"population": 5000, "confidence_pct": 95, "margin_pct": 5} returns {"required_sample": 357, "assumptions": "p=0.5 (max variance), random sampling"}.
Use before collecting data, to size a survey. Not for analyzing data already collected (stats_describe, confidence_interval). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "confidence_pct must be 90, 95, or 99"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| margin_pct | No | Acceptable margin of error percentage; above 0 and at most 50. Default 5. | |
| population | No | Total population size; 0 (default) means unknown or very large. | |
| confidence_pct | No | Confidence level; must be 90, 95, or 99. Default 95. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations that declare readOnly and idempotent, description adds error behavior (never protocol error, returns structured error), retry safety, and method details (maximum variance, finite-population correction). No contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Every sentence serves a purpose: definition, method, usage, error handling, safety. Well-structured and compact.
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 tool complexity (3 optional params, output schema exists), description covers purpose, method, example, error handling, retry guidance, and usage constraints. Output schema is implied via example.
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. Description adds value by explaining usage context for each parameter (e.g., 'population=0 means unknown'), providing a concrete example, and clarifying valid confidence_pct values.
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 calculates survey sample size with specific verb-resource, and explicitly distinguishes from siblings confidence_interval and stats_describe.
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?
Clearly states when to use ('before collecting data, to size a survey') and when not to, naming specific alternative tools for data already collected.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Describe a numeric dataset: center, spread, quartiles, and outliers. FREE.
Typical input {"numbers": [12, 15, 14, 90, 13]} returns {"n": 5, "mean": 28.8, "median": 14.0, "std_dev": ..., "min": 12, "max": 90, "q1": ..., "q3": ..., "iqr_outliers": [90], "skew": "right (mean > median)"}.
Use as a first summary of one numeric dataset. Not for interval estimates (confidence_interval) and not for planning a study (sample_size). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "no numbers"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| numbers | Yes | The dataset as a list of numbers; at least 1 value. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds significant value beyond annotations: it explicitly states the tool is read-only and idempotent (matching annotations), and crucially discloses error behavior ('never raises a protocol error — it returns {"error": ...}'). This behavioral detail would not be inferred from annotations alone.
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 (three paragraphs) with no wasted words. It front-loads the purpose, then provides a clear example, usage guidelines, and error handling. 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 presence of an output schema and the tool's moderate complexity, the description covers all necessary aspects: input, output, usage scope, limitations, error handling, and idempotency. An agent has full context to correctly invoke and interpret results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the parameter 'numbers' is already described in the schema. The description enhances meaning by showing a realistic input example and explaining the output structure (center, spread, quartiles, outliers). This provides context beyond the schema's raw type constraint.
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 combination ('Describe a numeric dataset: center, spread, quartiles, and outliers'). It provides a typical input and output example, and distinguishes from siblings by explicitly naming alternative tools (confidence_interval, sample_size) for different tasks.
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 ('first summary of one numeric dataset') and when not to use ('Not for interval estimates... not for planning a study...'). Also explains error handling and retry safety, giving clear context for invocation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
confidence_intervalformat_citationget_free_skillget_full_productget_full_skilllist_productssample_sizestats_describeClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user, then choose Claim with GitHub. An organization namespace such as io.github.acme/server also needs that organization to have installed the Glama AI GitHub App and approved its permissions, because GitHub discloses organization membership only to apps it has installed. Use HTTP or DNS when it has not.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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Add one secure layer between your agents and this server.
Each tool targets a distinct function: statistics tools for descriptive, inferential, and planning; product tools for listing, free skill, paid skill, and full product; and citation formatting. No overlap or ambiguity.
Most tools follow a verb_noun or noun_verb pattern, but there is a mix: some are noun phrases (confidence_interval, sample_size, stats_describe) while others are verb phrases (format_citation, list_products). The naming is clear but not perfectly uniform.
With 8 tools, the server covers two functional domains (statistics and product browsing) without being bloated. Each tool serves a clear purpose, and the count feels well-scoped.
The product domain is covered thoroughly (list, free skill, paid skill, full product). The statistics domain is missing hypothesis tests and more advanced analyses, but the included tools cover basic descriptive, confidence intervals, and sample size planning, which is reasonable for a 'research desk' scope.