Verified NFL stats and Sleeper league context. Every figure carries its own source rank.
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.
Average 3.8/5 across 13 of 13 tools scored. Lowest: 2.9/5.
Each tool targets a distinct resource or action: metrics, comparisons, leaders, Sleeper league operations, and verification. The clear separation between general stats tools and Sleeper-specific tools prevents confusion.
All tools use lowercase snake_case with a consistent verb-noun pattern (get_, list_, query_, compare_, verify_). The sleeper_ prefix cleanly groups Sleeper-related tools without breaking the pattern.
13 tools is well-scoped for a stats and fantasy context server. The count supports a full workflow (describe metrics, query leaders, compare entities, access fantasy leagues) without redundancy or bloat.
The tool surface covers the core domain thoroughly: metric discovery, player/team metrics, leaders, comparisons, verification, and Sleeper league data. Minor gaps exist, such as no direct schedule or news tool, but these are not dead ends.
compare_entitiesCompare two players or teamsATwo players or two teams shown side by side on the verified metrics both have, with each side's source rank. States both figures and stops: it names no winner, no margin and no recommendation.
| Name | Required | Description | Default |
|---|---|---|---|
| a | Yes | ||
| b | Yes | ||
| season | No | ||
| metrics | No | ||
| entity_kind | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds valuable behavioral context beyond these: it explicitly says the output shows both figures and stops, naming no winner, margin, or recommendation. This clarifies the tool's output behavior and its limitations, which is not captured in the 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 two sentences, front-loaded with the core comparison functionality. Every phrase earns its place, and the explicit list of non-behaviors is concise and valuable. There is no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema, the description does a good job of outlining the tool's core behavior and limitations. It covers entity types, metrics, and the fact that no verdict is given. However, it lacks details on the season parameter and what 'source rank' means, so the agent might still have minor uncertainty about invocation parameters. Overall, it is sufficiently complete for a comparison 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?
With 0% schema description coverage, the description must compensate for parameter meanings. It clarifies entity_kind through 'players or teams' and implies the 'metrics' parameter via 'verified metrics both have', but it does not explain the 'season' parameter or the exact format of 'a' and 'b'. Thus, the description adds some meaning but is incomplete, leaving ambiguity around the season parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: showing two players or teams side by side with verified metrics and source ranks. It also specifies what the tool does not do (no winner, margin, or recommendation), which fully distinguishes its purpose from any comparison tool that might render a verdict. This goes beyond a simple restatement of the title.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool: when comparing two entities on metrics they both have. It also implicitly excludes use cases where a judgment or winner is needed by stating it stops at stating figures. However, it does not explicitly mention alternative sibling tools or conditions that would make another tool more suitable, so it lacks full exclusionary guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_backfieldA team's backfieldAMuffed's published handcuff page for one team's backfield, plus the current depth chart and injury status from Sleeper. Answers who backs up a starter, and who replaces one who is out.
| Name | Required | Description | Default |
|---|---|---|---|
| team | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
While annotations indicate read-only, idempotent, and open-world behavior, the description adds value by specifying data sources (Muffed's handcuff page and Sleeper) and the exact content (depth chart, injury status). This goes beyond the structured annotations and helps set expectations 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 two sentences, front-loaded with the core purpose and data sources, and includes a practical use-case question. Every phrase contributes meaning without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the single parameter, no output schema, and rich annotations, the description adequately explains what the tool does and what it returns. It could mention the response format or structure, but for a simple lookup tool the provided context is sufficient to understand and invoke it.
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 one parameter 'team' with a full enum of NFL team codes but no property description. The tool description clarifies that the parameter selects a single team's backfield, which is sufficient compensation. The enum itself provides the allowed values, so the meaning is clear.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: it retrieves a team's backfield handcuff information, depth chart, and injury status. It uses specific verbs like 'answers' and identifies the resource as a specific team's backfield, distinguishing it from sibling tools focused on leagues, players, or metrics.
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 implicitly defines when to use the tool (to find backup running backs and replacements for injured starters). It does not explicitly name alternatives or exclusions, but the context is clear and distinct from sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_current_insightsMuffed's current readsAMuffed's published editorial reads on what is real in the NFL right now — each one a dated, sourced claim with its supporting figures, from the /trends board.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| subject | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safe read nature is covered. The description adds valuable behavioral context: insights are 'dated, sourced claim with its supporting figures,' which gives the agent a better understanding of the returned data structure and provenance, despite no mention of auth or rate limits.
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 is front-loaded with the main subject and provides essential information without unnecessary words. It is concise and directly conveys the tool's purpose and source.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is relatively simple with two optional parameters and no output schema. The description gives a good overview of the data content but misses parameter semantics and does not describe the return format or how limit/subject affect results. This is adequate but leaves room for improvement.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not mention the parameters (limit, subject) at all. Since there are two optional parameters, the description fails to explain their meaning or usage, leaving the agent to infer from the schema alone. This is a significant gap.
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 identifies the tool's purpose: retrieving Muffed's published editorial reads on the current NFL state. It specifies the resource ('editorial reads', '/trends board') and the nature of the content ('dated, sourced claim with its supporting figures'), distinguishing it from sibling tools focused on metrics, backfield data, or league info.
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 in the context of wanting current editorial insights from Muffed, but it does not explicitly state when to prefer this tool over alternatives or provide exclusions. There is no reference to other tools (e.g., for metrics use get_player_metrics), so guidance is implicit rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_metric_definitionWhat a metric meansBWhat a verified metric measures, which seasons it actually covers, how it is ranked, its licence and its attribution. Season coverage is often narrower than a decade — rushing yards over expectation, for instance, exists from 2018 onward.
| Name | Required | Description | Default |
|---|---|---|---|
| metric | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and non-destructive, and the description adds valuable context: season coverage is often narrower than a decade, with a concrete example (rushing yards over expectation from 2018). This clarifies a non-obvious data behavior without contradicting 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 two sentences, front-loaded with the key content and a concrete illustrative example. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter read-only lookup, the description covers the definition's content but omits return structure and error behavior (e.g., what happens for unknown/unverified metrics). Since there is no output schema, more details could help, but the description covers the essentials.
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 metric parameter is a single required string with no schema description, and the tool description does not explicitly document it. While the description implies the parameter is a metric name, it gives no format, allowed values, or examples for passing the parameter, so it fails to compensate for 0% 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 what a verified metric measures, its season coverage, ranking, licence, and attribution, giving a specific resource and scope. It distinguishes from list_metrics by focusing on a single metric's definition, though the verb is implied by the tool name rather than stated in the description.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not mention any alternatives or when to use this tool versus siblings like list_metrics or get_player_metrics. It only implies usage for understanding a metric, but offers no explicit guidance or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_player_metricsA player's verified figuresAEvery verified figure Muffed holds for one player or team, with the source's rank where one exists. Names are resolved before lookup; an unrecognised name returns the closest matches rather than a guess.
| Name | Required | Description | Default |
|---|---|---|---|
| metric | No | ||
| player | Yes | ||
| season | No | ||
| entity_kind | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint and idempotent annotations, the description discloses that names are resolved before lookup and unrecognized names return closest matches rather than guesses. It also notes the presence of source ranks where available. This adds useful behavioral context without contradicting 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 two sentences, front-loaded with the core purpose, and adds name-resolution behavior without fluff. Every sentence contributes meaningful 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 has four parameters and no output schema, the description provides only a high-level purpose and one behavioral nuance. It does not explain what a 'verified figure' entails, how the rank is represented, or the structure of the returned data, making it less complete than ideal.
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 0% parameter description coverage, and the tool description does not compensate. It mentions 'player or team' which indirectly references entity_kind, but metric, season, and the exact format of player names are unexplained. With four parameters and no schema descriptions, the description fails to add meaning to most parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool provides 'Every verified figure Muffed holds for one player or team', which conveys a specific retrieval purpose. It distinguishes from siblings by emphasizing 'verified figures' and 'source's rank', though it does not explicitly name alternative tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving verified figures and includes name-resolution behavior, but does not explicitly state when to use this tool versus alternatives. There is no mention of when not to use it or which sibling tools would be better suited.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_metricsList verified metricsAThe metric keys Muffed can answer on, with their labels and the seasons each one covers. Returns keys and coverage only, never figures.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| search | No | ||
| season | No | ||
| entity_kind | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and non-destructive. The description adds the behavioral constraint 'never figures', clarifying it returns metadata only. This is valuable beyond annotations, though it doesn't discuss auth or rate limits.
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, focused sentence that leads with the core purpose and then clarifies the return scope. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description explains the return format (keys, labels, coverage) and scope (no figures), which is good for a simple list tool. However, it omits any mention of parameters or filtering behavior, making it incomplete for a tool with four filter parameters and no output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description does not explain any parameters. The parameters limit, search, season, and entity_kind are left undocumented, with no guidance on how they filter results. The description fails to compensate for the schema gap.
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 lists metric keys with labels and seasons covered, and explicitly says it returns only keys and coverage, never figures. This distinguishes it from siblings like get_player_metrics or get_metric_definition which presumably return values or definitions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for discovering available metrics and their coverage, which is clear context. However, it does not explicitly name alternatives or exclusion criteria, so it falls short of the 'explicit when/when-not' level.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
query_stat_leadersVerified stat leadersATop entities on one verified metric for one season, pre-sorted, each with the source's own rank and the population it was ranked against. Call list_metrics for the metric keys this accepts.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| order | No | ||
| metric | Yes | ||
| season | No | ||
| position | No | ||
| entity_kind | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that results are 'pre-sorted' and include 'the source's own rank and the population it was ranked against,' which adds behavioral detail beyond the annotations (readOnlyHint, idempotentHint, destructiveHint). It does not contradict annotations and provides useful context about output shape.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core action, and includes a useful cross-reference to list_metrics. Every sentence earns its place 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?
The tool has 6 parameters and no output schema, so the description should provide more context about return values and parameter behavior. It does mention rank and population, which is helpful, but leaves gaps about defaults, ordering behavior, and entity_kind usage. Adequate for basic understanding but not fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description carries the burden for parameter meaning. It only implies metric and season ('one verified metric for one season') but does not explain limit, order, position, or entity_kind. The pointer to list_metrics helps for metric, but the other parameters remain undocumented.
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 identifies the tool's function: retrieving top entities for a single verified metric and season, with sorting and rank information. It distinguishes itself from sibling tools by emphasizing 'verified metric' and 'source's own rank,' making it unique from list_metrics or compare_entities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a clear use case (top entities for a verified metric/season) and explicitly directs users to list_metrics for metric keys, which is helpful for selecting the right input. It does not explicitly state when not to use this tool or mention alternatives, but the context is sufficient for an agent to decide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
sleeper_find_leaguesFind a Sleeper user's leaguesALists the NFL leagues a Sleeper username belongs to, so a league id can be picked for the other league tools. Muffed stores nothing about who called it.
| Name | Required | Description | Default |
|---|---|---|---|
| season | No | ||
| username | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds a privacy note ('Muffed stores nothing about who called it') which is useful context, but it does not describe return format or error behavior. With annotations covering the safety profile, this is consistent with a score of 3.
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: the first front-loads the main purpose and the second adds a meaningful privacy statement. No redundant or extraneous content is present.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has a required (username) and optional (season) parameter, no output schema, and useful annotations. However, the description does not explain the optional season parameter, the return format, or potential error cases, leaving significant gaps for an agent to use 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?
Schema description coverage is 0%, and the description does not explain any parameters. It indirectly references 'username' but completely omits the 'season' parameter, leaving the optional parameter undocumented. This fails to compensate for the lack of schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Lists') and resource ('NFL leagues a Sleeper username belongs to'). It also distinguishes itself from sibling league tools by indicating its role in picking a league id for 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?
The description implies when to use the tool ('so a league id can be picked for the other league tools') and suggests a workflow order. However, it does not explicitly state when not to use it or mention any alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
sleeper_get_leagueA Sleeper league's setupAOne league's name, season, size, current week, detected scoring format, and the manager-to-roster map. Everything downstream reads better once this has been called.
| Name | Required | Description | Default |
|---|---|---|---|
| league_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior, so the description need not repeat those. It adds value by specifying the exact data payload (manager-to-roster map, detected scoring format), and its 'downstream reads better' note implies a non-mutating, dependency-free operation. No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, zero redundant words. The first sentence front-loads the return payload; the second delivers a usage hint. 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?
For a simple read-only getter with one obvious parameter and strong annotations, the description covers the return payload and provides usage context. It doesn't detail error scenarios or distinguish from sibling getters, but those are not essential here.
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 sole parameter league_id is not described in the description, and schema_description_coverage is 0%. However, the tool name and 'One league's...' make the parameter's role obvious. Still, the description does not explicitly document the ID format or that it is required, relying on the schema pattern.
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 enumerates the returned league attributes (name, season, size, current week, scoring format, manager-to-roster map), making it clear this retrieves a single league's configuration. It distinguishes from sister tools like sleeper_find_leagues by focusing on a specific league rather than searching, though no explicit comparison is made.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'Everything downstream reads better once this has been called' indicates this is a preliminary/setup call to be used before other league operations. It does not explicitly mention alternatives or exclusions, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
sleeper_matchup_previewVerified context for a matchupABoth sides of a league matchup, with the verified figures Muffed holds for the players involved and each team's record. Muffed does not project outcomes, name a winner, or recommend a lineup.
| Name | Required | Description | Default |
|---|---|---|---|
| team | Yes | ||
| week | No | ||
| league_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only/idempotent behavior, and the description adds context that this provides verified figures and doesn't make projections, which is useful for setting expectations 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?
Two sentences, front-loaded with the core purpose, and no wasted words. The description is concise and reads well.
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?
It gives purpose and boundaries, but without an output schema or parameter explanations, it's incomplete for an agent to know exactly what to expect in return or how to construct inputs properly.
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) and the description does not explain the meaning or format of league_id, team, or week. It only implies league context, leaving the agent to infer parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool provides both sides of a league matchup with verified figures and team records, and explicitly distinguishes itself from projection tools by stating what it does not do. This differentiates it from sibling story/insight 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?
It implies when to use (when you need verified matchup context) and explicitly states it does not project outcomes or recommend lineups, which serves as a when-not. However, it doesn't name alternative tools for projections.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
sleeper_roster_storiesA roster's verified storiesCOne team's roster mapped to the verified figures and published Muffed surfaces for each player. Players Muffed does not cover are listed as uncovered rather than omitted.
| Name | Required | Description | Default |
|---|---|---|---|
| team | Yes | ||
| league_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds valuable context by specifying that players not covered by Muffed are listed as 'uncovered' rather than omitted, clarifying the tool's completeness behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief and front-loaded with the core concept. However, it relies on the unexplained term 'Muffed' and the phrase 'published Muffed surfaces' is awkward, slightly compromising clarity for conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no parameter explanation, the description does not fully cover what an agent needs to know. It lacks return format, parameter semantics, and clarity on the 'Muffed' concept, though the simple structure and annotations partially compensate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain the meaning or format of league_id or team. It only vaguely references 'one team's roster' without connecting to the required parameters, leaving the agent without sufficient information to construct valid arguments.
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 maps a team's roster to verified figures and published Muffed surfaces, which is a specific resource and operation. It distinguishes itself from sibling tools like sleeper_get_league or sleeper_standings_story by focusing on roster-specific data, though the term 'Muffed' is unexplained.
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?
No guidance is given for when to use this tool versus alternatives. The description implies input via league_id and team but does not state prerequisites, exclusions, or when other Sleeper tools might be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
sleeper_standings_storyA league's standingsALeague standings derived from Sleeper's roster settings — records, points for, and points against where available. Sleeper publishes no standings endpoint, so these are computed from the roster rows.
| Name | Required | Description | Default |
|---|---|---|---|
| league_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish safety (readOnlyHint, idempotentHint, destructiveHint=false). The description adds valuable transparency by explaining that standings are computed from roster rows and that points against may not always be available ('where available'). This helps the agent understand data derivation and potential limitations, exceeding the annotation baseline.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long, front-loaded with the core purpose, and immediately followed by a brief explanatory note about the derivation method. Every word earns its place; there is no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, no output schema, strong annotations), the description is complete. It explains the computation method, the data fields returned, and a limitation ('where available'). With no output schema, it successfully summarizes the return content. This is sufficient for an agent to use the tool effectively.
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 0% description coverage, so the description must compensate. However, it never mentions the required league_id parameter or its role. The parameter name is self-explanatory, but the description adds no meaning beyond the schema, leaving the agent without explicit guidance on what input to provide or expected formats.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: computing league standings from roster settings, specifying the output (records, points for, points against). It also distinguishes itself from siblings by explaining that Sleeper has no standings endpoint, so these are derived from roster rows. This is a specific verb+resource with clear scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool: whenever league standings are needed, because Sleeper does not provide a direct endpoint. It implies the tool is the appropriate choice for this purpose, though it does not explicitly reference alternative tools or exclusions. The context is clear and no misleading guidance is present.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
verify_claimCheck a stated figure against MuffedAChecks structured claims against Muffed's verified panel. Each claim comes back confirmed, wrong (with the actual value), or unverifiable (with the metric's real season coverage). Takes structured fields only, not sentences.
| Name | Required | Description | Default |
|---|---|---|---|
| claims | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the read-only and idempotent annotations, the description discloses the exact return outcomes (confirmed, wrong with actual value, unverifiable with season coverage), providing valuable behavioral detail. No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences: the first states purpose and outcomes, the second sets a usage constraint. Every word earns its place, 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?
Despite lacking an output schema, the description fully explains the return possibilities and the input restriction. It also covers the verification semantics that are essential, and schema details like required fields and limits remain in the schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With schema description coverage at 0%, the description must compensate, but it only states 'structured fields only' without explaining the fields (metric, entity, value, season, entity_kind). The schema names and types exist, but the description adds minimal semantic clarity beyond that.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool checks structured claims against Muffed's verified panel, with a specific verb ('checks') and resource. It also differentiates from siblings by focusing on verification with three outcome types (confirmed, wrong, unverifiable), not comparison or lookup.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly says 'Takes structured fields only, not sentences,' which tells the agent when to use it (for structured claims) and when not to (for natural language). It does not name alternative tools but implies the scope.
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.
No comments yet. Be the first to start the discussion!