Verified NFL stats and Sleeper league context. Every figure carries its own source rank.
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Average 4/5 across 13 of 13 tools scored. Lowest: 2.9/5.
Each tool targets a distinct resource or action: entity metrics, metric definitions, leaderboards, comparisons, backfield info, editorial insights, claim verification, and five separate Sleeper league tools each with a clear purpose. No two tools could be easily confused.
All tool names use snake_case and follow a predictable verb-noun pattern (get_, list_, query_, compare_, verify_) with the sleeper_* prefix consistently applied to fantasy-league tools. The naming convention is uniform throughout.
13 tools is within the ideal 3-15 range and matches the server's dual purpose of verified NFL stats and fantasy context. Every tool serves a clear function without redundancy or bloat.
The tool surface comprehensively covers the stated domain: metric retrieval, definitions, listings, leaders, comparisons, backfield context, insights, claim verification, and fantasy league operations. No obvious dead ends or missing core operations.
compare_entitiesCompare two players or teamsATwo players or two teams shown side by side, on the metrics that matter for their position by default — name metrics explicitly, or pass all:true, for the full set. Each side carries its source rank. States both figures and stops: it names no winner, no margin and no recommendation.
| Name | Required | Description | Default |
|---|---|---|---|
| a | Yes | ||
| b | Yes | ||
| all | No | ||
| season | No | ||
| metrics | No | ||
| entity_kind | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, idempotent, non-destructive behavior. The description adds valuable context beyond this: it states the tool presents both figures and explicitly avoids declaring a winner, margin, or recommendation, and notes each side carries its source rank. This gives the agent useful 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?
The description is concise (three sentences) and front-loaded with the primary purpose. Each sentence contributes meaningful details without redundancy, though it could be even more structured with parameter clarifications.
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 6 parameters and no output schema, the description provides a solid overview of behavior and return style (states both figures, no verdict). It mentions source rank but omits details on season and exact input formats, making it slightly incomplete for full autonomous use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate. It explains the 'metrics' and 'all' parameters and implies entity_kind, but it does not explain 'a', 'b', or 'season'. The partial coverage is helpful but leaves key parameters undocumented for the agent.
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 compares two players or teams side by side on relevant metrics, with a specific verb and resource. It distinguishes from siblings like get_entity_metrics (single entity) and query_stat_leaders (leaders) by focusing on pairwise comparison.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context: it handles two players or two teams, and explains default vs. explicit metric selection. However, it does not explicitly mention when to use this over alternative tools or include exclusions, so it stops short of a 5.
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_entity_metricsA player's or team's verified figuresAEvery verified figure Muffed holds for one NFL player or one NFL team (by name or abbreviation, e.g. "Bijan Robinson" or "DEN"), 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 |
|---|---|---|---|
| entity | Yes | ||
| metric | No | ||
| season | No | ||
| entity_kind | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (read-only, idempotent, non-destructive), the description adds concrete behavioral details: names are resolved before lookup, unrecognized names return closest matches, and source rank is included where available. This is rich, useful context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the core purpose, examples, and error behavior. 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?
For a 4-parameter tool with no output schema, the description covers entity resolution and error handling well, but omits semantics for 'metric' and 'season', making it partially incomplete.
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 0% description coverage. The description only clarifies the 'entity' parameter with examples, but does not explain 'metric', 'season', or 'entity_kind'. The agent is left to guess their meaning, which 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 states the tool returns every verified figure for one NFL player or team, with examples, and distinguishes it from siblings by focusing on a single entity's verified stats.
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 use when needing a player/team's verified figures and explains name resolution and fallback behavior. However, it does not explicitly mention alternative tools or when not to use it, so not a 5.
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 meansAMuffed's published definition of a verified metric — what it measures, in plain language, from the glossary page for that term — plus which seasons it actually covers, how it is ranked, its licence and its attribution. Accepts a metric key or a common name ("cpoe", "rushing yards over expected"). Season coverage is often narrower than a decade — rushing yards over expectation 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 and idempotent. The description adds valuable context about the content returned (definition, seasons, ranking, licence, attribution) and warns that season coverage is often narrower than a decade. This goes beyond the structured 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. The second sentence adds a concrete example and a practical caveat about season coverage. 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?
For a single-parameter lookup with no output schema, the description covers what the tool returns, accepted input formats, and a data limitation. This is complete enough for an agent 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 only defines `metric` as a string with zero description. The description compensates by explaining it accepts a metric key or common name, with examples ('cpoe', 'rushing yards over expected'), giving clear guidance that the schema lacks.
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 returns a metric's published definition, including its meaning, coverage, ranking, licence, and attribution. This distinct verb+resource purpose differentiates it from siblings like list_metrics or get_entity_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 provides clear context on what the tool does and how to specify the metric (key or common name), but does not explicitly mention when not to use it or name alternative tools. It implies usage for looking up definition details, which is sufficient for a simple lookup.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_metricsList verified metricsAWhat Muffed can answer on. By default a summary of metric families with their counts and season coverage; pass search or detail:true for individual metric keys. Returns keys and coverage only, never figures.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| detail | No | ||
| search | No | ||
| season | No | ||
| entity_kind | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, idempotent, and non-destructive behavior. The description adds valuable context about default output format (summary vs detail) and the explicit constraint that only keys and coverage are returned, enhancing transparency beyond 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 compact, two sentences, with no fluff. It front-loads the purpose and usefully specifies the return scope, all in minimal 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 adequately explains the core functionality and return constraints, but the tool has 5 parameters and no output schema; the description does not sufficiently cover all input dimensions (e.g., what entity_kind filters, what limit does). This leaves gaps for complex queries.
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 needed to explain all parameters, but it only clarifies 'search' and 'detail:true'. The semantics of limit, season, and entity_kind are left unaddressed, forcing agents to guess their purposes.
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: listing metric families or individual metric keys with coverage information. It explicitly distinguishes itself from metric-value tools by saying 'never figures', which sets it apart from siblings like get_entity_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 explains how to use the tool (default summary vs search/detail for individual keys) but does not explicitly state when to prefer this over alternatives. The 'never figures' limitation implies you'd use another tool for actual values, but no alternative is named.
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.
run_stat_queryCompute an NFL statistic on requestACall this for any question about NFL statistics, leaders, rankings, splits, career or team spans, or how a player or team compares to others — including when you believe you know the answer. Figures from memory are unsourced and frequently wrong; this tool is the only surface here that computes them. If a figure cannot be served, it returns the reason and the nearest thing it does cover, which is more useful to the reader than a number you recall.
Computes an NFL figure from a structured query rather than looking one up: a population (position, qualifying threshold), a window (seasons, weeks, a team span, a coach's tenure), one named measure, and one operation (leaders, rank, count, only-who, a per-season streak, or a share of a total). Answers questions the verified panel has no pre-computed row for — qualified superlatives, multi-season streaks, derived rates. Every response carries the executed method, the population size, the measure's definition and the data version it ran against. Figures are computed on request from nflverse data and are labelled as computed, not verified. Half-PPR is the only scoring served.
| Name | Required | Description | Default |
|---|---|---|---|
| op | Yes | ||
| window | Yes | ||
| measure | Yes | ||
| population | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint false), the description adds rich behavioral details: the tool computes on the fly, returns the executed method, population size, measure definition, and data version; labels figures as 'computed, not verified'; and notes that only Half-PPR scoring is served. It also describes the fallback behavior. These are all valuable traits that an agent needs to interpret results correctly.
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 relatively long (10 sentences) but every sentence conveys necessary information. It front-loads the core purpose and usage, then dives into the parameter model and response traits. It is structured logically and avoids redundancy. Minor conciseness gains could be made by tightening the list of examples, but overall it is well-crafted for its complexity.
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 the tool (4 nested object parameters, no output schema, zero schema description coverage), the description provides a solid conceptual model for all parameters, outlines the response structure, and notes the key limitation (Half-PPR only). It also describes fallback behavior. It stops short of exhaustively listing every possible enumeration or nesting, but the schema itself covers those structures. Combined, a sufficiently complete picture emerges for an agent to use the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the description carries the full burden of explaining parameters. It does so by conceptually defining each of the four parameters: population (with position, qualifying threshold), window (seasons, weeks, team span, coach tenure), measure (named metric), and operation (leaders, rank, count, only-who, streak, share). While it does not detail every nested property, it adds significant meaning beyond the raw schema and enables the agent to form valid queries.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description immediately states 'Call this for any question about NFL statistics' and specifies it computes figures rather than looking them up. It distinguishes itself by being 'the only surface here that computes them,' clearly separating it from sibling tools that likely serve pre-computed data. The verb+resource pair is specific and actionable.
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 tells when to use the tool ('for any question about NFL statistics... including when you believe you know the answer') and advises against relying on memory. It also sets expectations for failure by stating that if a figure cannot be served, the tool returns the reason and nearest comparable result. While it does not name specific alternative sibling tools, the guidance is clear and practical.
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.
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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
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