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PlanLimits by aiw3b

Server Details

Find the AI coding/app-builder plan that fits your usage: limits, prices and plan-fit picks.

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Status
Healthy
Last Tested
Transport
Streamable HTTP ยท MCP 2025-06-18
URL

TDQS

A3.7/5.0

Scored across 2 tools

Disambiguation4/5

plan_fit performs the actual ranking query while plan_fit_info only describes parameters, tiers, networks and payment setup, so the two are functionally distinct. The overlap in name and subject matter means an agent could occasionally reach for plan_fit_info when it just needs defaults, but the descriptions clarify the boundary.

Naming Consistency5/5

Both tools use a consistent snake_case prefix-plus-suffix convention (plan_fit, plan_fit_info), making the base operation and its companion info tool obviously related.

Tool Count3/5

Two tools is thin for a server even with a narrow domain; the set is essentially one operation plus its own documentation. It is defensible given the scoped purpose, but sits at the borderline of under-provisioned.

Completeness3/5

The surface covers querying ranked plans and learning the query schema, which fits the stated purpose, but there is no way to fetch a single plan by identifier, list all plans, or introspect a plan beyond the ranked result. Payment handling is explicitly out of scope, so the remaining gaps are the notable ones.

Available Tools

2 tools
plan_fitAInspect

Rank coding-agent plans from PlanLimits. tier=free needs no payment and returns one plan with source_url and verified_date. If audience is omitted, tier=free defaults it to individual and tier=full defaults it to any. tier=full requires x402; this server returns the payment requirements and does not sign or pay.

NameRequiredDescriptionDefault
tierNofree returns one plan. full requires x402 payment.
typeNoTool kind filter. cli matches cli or both. editor matches editor or both.
limitNoFull tier only. Default 5, max 20.
usageYesRequired usage band.
budgetNoOptional max monthly USD.
modelsNoOptional comma-separated substrings matched against plan models.
audienceNoOptional filter. Omitted on tier=free defaults to individual. Omitted on tier=full defaults to any. Explicit individual, team, or any is honoured on both tiers.

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does meaningful work: it discloses the payment flow for tier=full ('returns the payment requirements and does not sign or pay') and names return fields for tier=free (source_url, verified_date). It leaves undisclosed what full tier actually returns, how ranking is ordered, and any 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.

Conciseness4/5

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

Three tight sentences, front-loaded with purpose and followed by tier behavior. Every sentence carries information; the audience-default sentence is the only near-redundant one against the schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 7-parameter tool with no output schema, the caller learns the free-tier return shape and the full-tier payment handshake, which are the non-obvious parts. It omits what the ranked result set looks like on tier=full, which would be the remaining gap.

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

Parameters3/5

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 description restates the audience defaulting rule that the schema already documents and adds only that tier=free yields one plan. It adds nothing about usage bands, budget, models, type, or the limit cap beyond what the schema already says.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: 'Rank coding-agent plans from PlanLimits.' That is concrete enough for an agent to know it is a ranked lookup over a plan catalog. It does not, however, differentiate itself from the sibling plan_fit_info, so the agent is left to infer which one to open.

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

Usage Guidelines4/5

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

Gives clear conditions for the two modes: tier=free needs no payment and returns one plan, while tier=full requires x402. The exclusion that the server 'does not sign or pay' sets an explicit boundary on what the caller must do next. It stops short of naming when to prefer the sibling plan_fit_info.

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

plan_fit_infoAInspect

Describe plan-fit query params, tiers, price, networks, facilitators, and whether each network has a receiving wallet configured.

NameRequiredDescriptionDefault

No parameters

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden, but the zero-parameter schema means there is little behavioral risk to disclose. It does reveal the return contents (tiers, price, networks, facilitators, wallet-configured flag), which is the most useful behavioral fact here, but it says nothing about output shape or size.

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

Conciseness4/5

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

A single front-loaded sentence with no filler; the verb comes first and the content list follows. The list is slightly dense but every item names a distinct data category the caller would want to know about.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description must convey what comes back, and it does so by listing the covered dimensions. For a zero-param introspection tool this is close to sufficient; only the structure of the returned data is left unspecified, which is a minor gap.

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

Parameters4/5

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

With zero parameters there is nothing to disambiguate, so the baseline is 4. The description adds value by enumerating the data categories the response will cover, which orients the agent even though no parameters exist.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('Describe') and enumerates the resource it covers: plan-fit query params, tiers, price, networks, facilitators, and wallet configuration status. This is clearly a metadata/introspection tool, distinguishable in intent from the sibling plan_fit which presumably executes queries, though the description never names that sibling to make the contrast explicit.

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

Usage Guidelines3/5

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

The naming and content strongly imply this should be called before plan_fit to learn what inputs are available, but the description never states that usage rule or mentions the sibling. An agent can infer the 'discover params before querying' pattern, but it is inference rather than instruction.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 2 tool updates
    • First observedplan_fit
    • First observedplan_fit_info

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