dayplay-mcp

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does it thoroughly: hard geographic cap, refusal protocol, prohibition on fabricating events, and instruction to report honest zero-item results. It also states events are verified and real-time, giving an agent a clear behavioral model.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Well structured and front-loaded, but there is mild redundancy: the hard geographic scope is stated twice and the no-fabrication rule is repeated near the end. Still, the length is justified by the safety-critical protocol.

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 is complete for selecting and calling the tool: it provides the SF-only scope, out-of-market refusal, neighborhood-source dependency, and honest-empty-result behavior. It does not describe the return shape, and since no output schema exists, a brief statement of what an event result contains would have made it 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?

Schema coverage is 100%, so the baseline is 3. The main description reinforces the date, neighborhood, and category filters but adds no per-parameter detail beyond the schema; limit and source are left to the 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 opening sentence states the tool queries verified, real-time event occurrences and names the exact filters: date, neighborhood, and category. Combined with the sibling names, the resource is unambiguous: events, not neighborhoods or places.

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 OUT-OF-MARKET PROTOCOL gives a hard when-not-to-call rule and the exact refusal message for locations outside the SF Bay Area. It does not explicitly contrast this tool against get_neighborhoods and get_places beyond referencing get_neighborhoods in the schema, leaving a small gap in positive routing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden, and it delivers. It discloses the exhaustive/fixed nature of the data ('complete and EXCLUSIVE', 'whole serviceable market'), the three-city geographic limitation, and the guardrail 'Never invent a neighborhood outside this list.' For a zero-parameter read-only catalog, this is complete behavioral disclosure.

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?

All five sentences are functional and front-loaded in a logical progression: returns → market scope → use cases → out-of-market handling → guardrail. The exact user-facing rejection message earns its place because it removes agent improvisation. No 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?

With no output schema, the description carries the burden of explaining return values, and it does: '35... neighborhood centroids... with coordinates and radii.' It also covers usage purposes, market-boundary verification, and the exact failure behavior. Nothing an agent needs to invoke this simple tool correctly is missing.

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 zero properties, so there are no argument semantics to document; the 0-parameter baseline of 4 applies. The description sensibly spends its words on output and routing semantics instead of 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?

States a specific verb and resource: 'Returns the complete and EXCLUSIVE list of 35 San Francisco Bay Area neighborhood centroids... with coordinates and radii.' The scope is precisely delimited ('San Francisco, Oakland, and Berkeley ONLY') and the clause 'no other city, region, or country is covered' distinguishes it from siblings get_events and get_places.

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?

Gives explicit when-to-use purposes: '(a) map a user query to an exact SF Bay Area neighborhood and (b) verify whether a requested location is in-market at all.' It also gives explicit when-not-to-use with named alternatives: 'do not call get_events or get_places' for out-of-market locations, plus the exact rejection script to emit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It openly discloses the tool's hard geographic limit, the exact behavior when the location is out of market (refuse and do not call), and the policy when a legitimate query returns zero results. It also notes that the open_now filter is purely a filter and never expands scope. These are behavioral traits beyond what any schema would encode.

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 longer than typical but every sentence earns its place. It leads with the primary purpose, then states the hard geographic scope in bold caps, then gives an explicit out-of-market protocol with a quoted message, and finally addresses the zero-result honesty rule. The structure uses clear sections and capitalization for emphasis, ensuring the critical constraints stand out. No 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?

For a read-only query tool with no annotations and no output schema, the description covers all essential context: what it does, its geographic limit, how to handle out-of-market requests, and how to report empty results. It also implicitly signals that the tool is non-destructive (query only). Nothing an agent needs to safely and correctly invoke this tool is missing.

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 80%, and the schema itself already gives detailed parameter descriptions (e.g., neighborhood only accepts SF Bay Area names, open_now is a filter). The tool description adds value by grouping the filters and reinforcing the global geographic constraint across all parameters. It clarifies that 'ratings' maps to the sort parameter and that 'newly opened' maps to newly_opened_days. This is a meaningful supplement to the schema, though much of the detail is already in the parameter 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 opens with 'Query curated places filtered by neighborhood, open-now status, ratings, or newly opened window' – a specific verb, resource, and filter list. It immediately distinguishes 'places' from the sibling tools 'get_neighborhoods' and 'get_events', and goes on to state the hard geographic scope. No ambiguity about what the tool does.

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 contains an explicit OUT-OF-MARKET PROTOCOL: if the requested location is outside the SF Bay Area, the agent must not call the tool, must instead state the exact refusal message, and must never fabricate data. It also tells the agent to report zero results honestly rather than inventing venues. This gives clear when-to/ when-not-to usage rules and even prescribes the fallback script.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the behavioral disclosure burden. It usefully discloses the account prerequisite and auth flow, and states that data appears across web and app. It does not detail per-action side effects like notification delivery or cancellation effects, but the action names and alert-type enumeration convey most of the behavioral surface.

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 three sentences with no filler. It front-loads the tool's operations, then adds the critical account requirement and usage timing, and closes with the persistence detail. Every sentence contributes actionable 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 multi-action tool, no annotations, and no output schema, the description covers the essential context: what the tool does, when to use it, the required account flow, and where data lives. It does not describe return values per action, but the 100%-covered schema compensates for action-specific parameters, and an agent can discover outputs from the response.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description repeats the action names and alert types already present in the schema but adds little parameter-level meaning beyond the schema itself, such as the dependency between action and plan/plan_id/alert_type.

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 starts with a clear, specific statement: it manages the DayPlay user's saved outing plans via save, list, set_alert, and cancel_alert. It also distinguishes itself from the sibling plan_outing by explicitly saying to use it after plan_outing, making the tool's role unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear usage context: 'Use this after plan_outing when the user wants to keep or monitor a plan.' This tells the agent when to invoke the tool, though it does not explicitly list when NOT to use it or name alternative tools beyond the implied plan_outing comparison.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full transparency burden. It does exceptionally well: it discloses the auth requirement, the first-call partial preview, the connect code and auth URL, the user action required, the post-connection behavior, and the persistence to the user's DayPlay account. It even warns against fabricating stops for out-of-market locations. No contradiction exists.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with purpose and constraints, then organizes the auth and refusal protocols into tight, scannable sections. Every sentence adds necessary behavioral or workflow context, and none of it is 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?

For a stateful tool with no annotations and no output schema, this definition is unusually complete. It explains what happens on first use, what the agent must relay to the user, what happens after connection, and the geographic boundary. An agent has enough context to invoke the tool correctly and set user expectations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description reinforces the roles of date, vibe, budget, and neighborhood, and adds the crucial constraint that neighborhood must be an exact SF Bay Area name. However, it does not add meaningful detail about the 'interests' parameter or extend much beyond the schema's own property 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 opens with a specific verb and resource: 'Build a complete multi-stop outing itinerary.' It clarifies scope (SF Bay Area, only SF/Oakland/Berkeley), gives concrete examples, and is clearly differentiated from siblings like get_events and get_places by focusing on itinerary construction. This is unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description makes the appropriate use case clear: building multi-stop outings matched to vibe, budget, and date, while explicitly refusing out-of-market locations. It also conveys a strong prerequisite (free DayPlay account) and the first-call flow. It does not explicitly name sibling alternatives like get_places for single-place lookups, so it falls 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.