面向 Productive.io API v2 的 MCP 服务器——为单个组织提供项目、任务、时间跟踪、资源规划、财务、CRM 和报表。
Productive 在 132 个资源上暴露了约 650 个操作。如果把这些变成 650 个 MCP 工具，会淹没任何客户端的工具列表，因此这个服务器是由生成的注册表驱动的十二个工具：工具是通用的，注册表知道每个资源真正接受什么。
发现 — productive_search_capabilities、productive_describe_resource、productive_check_connection、productive_describe_custom_fields
读取 — productive_list（筛选、排序、包含关系、分页，以及带分组的 26 个报表端点）、productive_get
写入 — productive_create、productive_update、productive_delete、productive_run_action（150 个命名动词：archive、restore、approve、close、copy、finalize、send……）、productive_track_time、productive_commit_operation
按用户认证（可选） — productive_connect、productive_status、productive_disconnect — 参见认证
从 productive_search_capabilities 开始。Productive 的资源名是它自己的——预算是 deal，看板列是 workflow_status，工时表审批位于 time_entries 上——而猜测会付出调用代价。
Related MCP server: productive-mcp-rb2
src/productive/registry.generated.ts 由 scripts/generate-registry.mjs 从 Productive 发布的 OpenAPI 文档生成，并已提交，因此 CI 永远不需要网络，规范变更会以可审查的 diff 形式呈现。对于每个资源，它记录筛选字段、排序键、报表分组键、可包含的关系、创建和更新的可写属性（并标记必填项），以及每个命名操作。
这正是让十二个工具保持诚实的原因。productive_describe_resource 返回某个资源的精确契约，每个参数在请求发出前都会对照它进行检查。
使用 npm run registry:generate 重新生成（添加路径参数以使用本地副本的规范）。如果手写的分类——风险等级、面向外部的标志、被阻止的操作——不再匹配规范中的任何路径，生成器会使构建失败，因此上游的重命名无法悄悄移除防护。
这里的一切都针对一个真实组织验证过，因为规范与 API 在关键之处存在分歧。
时间以分钟计，金额以最小货币单位计。 一条 2 的工时记录就是两分钟。返回时两者也都是这样。
未知的筛选、排序和包含关系会明确失败。 HTTP 400，附带 unsupported_filter、sort_param_unsupported、unsupported_include。因此在这里校验它们能提供更好的错误信息，而不是一道安全网。
未知的写入属性会静默失败。 使用拼写错误的属性进行 PATCH 会返回 HTTP 200 且不改变任何内容——与成功无法区分。因此，本服务器会拒绝资源未声明的属性，而不是报告一次并未发生的写入。这是注册表所做的唯一最有价值的事情。
每个字段上恰好有六个筛选运算符： contains、eq、gt、lt、not_contain、not_eq。规范为每个字段列出了四个，并省略了实际可用的 gt/lt；gte、lte、in、not_in、starts_with、ends_with、blank 和 present 都会被以 unsupported_filter_operation 拒绝。没有包含边界的比较，因此包含边界的范围需要使用资源自身的 after/before 或 <field>_after/<field>_before 筛选字段。
page[size] 上限为 200，并静默截断。 请求 500 会返回 200 且不报错。结果带有 total 和 nextPage，因此不会把一页误认为全部答案。
PATCH 是真正的部分更新。 省略的属性保持原值；无需重新发送整条记录。
data.type 不会被检查。 用 type: "projects" 修补任务会成功并应用更改。本服务器无论如何都会发送正确的类型。
返回 403 并提示组织 id“必须提供”，可能意味着它错了，而不是缺失。 同一个 no_organization_id 代码既涵盖缺失的请求头，也涵盖令牌无法访问的组织。
缺少的功能返回 404，而不是 403。 在没有该功能的组织上，/boards 会返回 404，看起来像路径错误。
删除可能是可恢复的。 已删除的任务会出现在 deleted_items 中，带有 item_type 和 item_id，并可通过该资源的 restore 操作恢复。仅对任务验证过——不要假设它对所有类型都成立。
GET /users 是唯一按调用者限定的端点。 它恰好返回一条记录——你——本服务器正是以此识别令牌的所有者。没有 /users/me；该路径会 404。注意 /organization_memberships：它不限定于固定的组织，而是列出调用者所属每个组织的成员关系，因此它的行数不是人数统计。
没有速率限制头。 只有 x-request-id，本服务器的错误信息会引用它。遇到 429 应退避，而不是探测限制。
四个开关，默认全部关闭。只读服务器是有用且安全的默认配置。
开关 | 覆盖范围 |
| 总开关。没有它，任何内容都不会被修改。 |
| 资金、定价、工资、客户收到的文档：发票、行项目、付款、账单、费用、采购订单、提案、合同、价格、费率卡、薪资、间接费用、税率、银行账户、子公司。 |
| 访问权限和全组织配置：人员、成员关系、权限集、团队、邀请、自定义字段、Webhook、集成、审批和时间跟踪策略。 |
| 在层级门禁之上的删除操作。 |
对于 Productive 来说，一个总开关是不够的：同一个 API 可以移动任务、开具发票和授予权限集，而这是三个不同的决策。一个被信任用于运行项目工作的服务器，不应因此就能发送发票。
PRODUCTIVE_ALLOWED_RESOURCES / PRODUCTIVE_DENIED_RESOURCES 可进一步缩小实例范围，并且同样适用于读取——一个限定于时间跟踪的实例也不应读取薪资。
无论开关如何，以下内容永远不会暴露： passwords、sessions、organization_subscriptions、未认证的 public/* 分享链接，以及 PATCH /users/{id}/update_password。这些内容不在注册表中，而不是被门禁控制，因此任何策略错误都无法重新打开它们。
普通的项目工作——任务、工时记录、预订、评论——通过一次调用写入。如果要求每次工时记录都进行握手，服务器将无法用于人们最常做的事情。
所有影响范围更大的操作都是分阶段的：工具返回确切的请求加一个哈希，但不发送任何内容，productive_commit_operation 仅在操作原样返回时才会执行它。这涵盖了财务和管理层级、所有删除、任何离开组织的操作，以及所有 bulk_* 操作——这些操作作用于筛选匹配的所有记录，因此它们也拒绝在没有显式筛选的情况下运行。
有六个操作被标记为 outward，因为它们在运行的那一刻就会触达组织之外的某个人：invoices.send、invoices.send_einvoice、people.invite、people.resend、organizations.resend_code，以及创建 invitation。
两种模式。两种模式都需要 PRODUCTIVE_ORGANIZATION_ID，它绝不是工具参数。
每个人关联自己的 Productive 令牌，这样 Productive 会应用他们的权限，并在他们的操作上记录他们的名字。
这一点在 Productive 上比在大多数系统上更重要。Productive 将工作归因于人：一条工时记录属于某个 person_id，每次更改都会在活动日志中盖上令牌所有者的印记——而这份日志正是客户发票据以辩护的记录。如果使用一个共享令牌，该日志会显示服务账户做了所有事情。
PRODUCTIVE_PER_USER_AUTH=true
PRODUCTIVE_TRUST_FORWARDED_USER=true
PRODUCTIVE_ENCRYPTION_KEY=<min 16 chars>
PRODUCTIVE_STORE_PATH=/data/store.json
PRODUCTIVE_PUBLIC_BASE_URL=https://productive.example.com
# PRODUCTIVE_API_TOKEN deliberately unset流程：
调用者运行 productive_connect，获得一个一次性链接，有效期 10 分钟，与其身份绑定。
他们打开链接，粘贴他们在 Productive 的设置 → API 集成下创建的令牌。令牌从他们的浏览器直接发送到服务器，因此永远不会进入对话记录——Productive 令牌等同于其整个账户的持有者凭证，而且如下文实测所示，通常可以访问多个组织。
在存储之前，服务器使用该令牌和本组织的 id 调用 GET /users。一次调用证明三件事：令牌有效、可以访问这个组织，以及它属于谁。然后页面会确认关联了哪个账户。
令牌使用 AES-256-GCM 静态加密，每个已验证身份一行。
需要注意的边界情况：
身份只来自网关。 仅当 PRODUCTIVE_TRUST_FORWARDED_USER=true 时才读取 X-MCP-User，绝不读取 MCP 客户端控制的任何内容。只有在网关根据已验证的令牌设置该请求头并剥离客户端提供的副本时，才启用它——否则调用者可以指定任意身份并冒充他们。
没有回退。 未注册的调用者会得到 NOT_CONNECTED，绝不会得到共享令牌，即使碰巧设置了 PRODUCTIVE_API_TOKEN。回退会把借来的权限交给他们，这正是该模式要消除的失败。
/productive/enroll 必须能被用户的浏览器访问，绕过 MCP 网关——浏览器无法携带网关的 bearer 令牌。将 PRODUCTIVE_PUBLIC_BASE_URL 上的 /productive/* 直接路由到容器。其安全性在于一次性、与身份绑定的状态令牌。
将 PRODUCTIVE_STORE_PATH 持久化到卷上，并保持 PRODUCTIVE_ENCRYPTION_KEY 稳定——更改它会导致所有已存储的令牌无法解密。
如果令牌自身的 Productive 邮箱与调用者的目录地址不同，页面和 productive_status 会明确报告，并且仍然连接。设置 PRODUCTIVE_REQUIRE_EMAIL_MATCH=true 可以改为拒绝。默认关闭，因为粘贴他人令牌的人已经持有该令牌，拒绝并不能带来多少安全性，而使用不同地址的 Productive 账户是完全可能的。
按用户认证区分的是权限和归属，而不是组织。组织固定仍然适用于每个人。
将 PRODUCTIVE_API_TOKEN 设置为一个令牌。简单，适合 stdio 或单一操作者——但每个调用者都会以该令牌所有者的身份行事，拥有他们的权限，并且 Productive 的活动日志会把每次更改都记在他们名下。
PRODUCTIVE_TRUST_FORWARDED_USER 在这里仍然有帮助：以人为对象的写入（一个时间条目、一次预订）默认使用已解析的调用者，而不是令牌的所有者；当地址匹配不到任何人或匹配到多个人时，服务器拒绝猜测。productive_check_connection 无论如何都会指明令牌的所有者，因此归属永远不会出人意料。
一个实例恰好服务一个组织。X-Organization-Id 来自环境变量，绝不是工具参数，因此任何代码路径——包括通用工具——都无法访问另一个租户。为第二个组织运行第二个实例；镜像是相同的。
这并非理论上的说法。单个令牌经常能访问多个组织：在开发此功能所用的账户上，GET /organizations 返回了三个组织，而仅切换请求头就能在它们之间切换（另外两个返回 403 subscription_expired，而不是“not found”）。请求头就是整个边界，这就是为什么它被固定下来而不是作为参数传入——也是为什么已注册的按用户令牌在存储之前会针对这个组织进行验证。
参见 .env.example。两个必需的变量是 PRODUCTIVE_API_TOKEN（Productive 中的 Settings → API integrations；它继承创建用户的权限）和 PRODUCTIVE_ORGANIZATION_ID（你的 Productive URL 中的数字 id）。
设置 PRODUCTIVE_AUDIT_LOG 以在每次变更尝试时追加一行 JSON，包括被策略拒绝的尝试。请求体有意不被记录：它们携带薪资、费率和个人数据；而审计跟踪必须像源系统一样严密保护，且往往不会被阅读。
npm install
npm run dev # stdio
npm run dev:http # streamable HTTP on :3000/mcp (stateless), /healthz open
npm test
npm run smoke:live # reads a real organization; stages one write, commits nothingDocker 镜像：ghcr.io/borgels/mcp-server-productive（在推送到 main 时发布）。
Apache-2.0。
Verify the token and organization and report what this deployment may do. Also names the person the token belongs to — every change Productive records is credited to them, whoever asked — and the caller identity, when a gateway forwards one.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already signals safety, and the description adds relevant behavioral context: token identity is attributed to every recorded change, and caller identity may be forwarded by a gateway. This goes beyond the annotation without contradicting it.
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 with the core action first, followed only by consequential detail about attribution and caller identity. There is no fluff or repetition of the title.
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 parameterless tool, the description conveys the main outputs: deployment capabilities, token owner, and caller identity. It does not specify exact response formatting or failure behavior, but the tool's simplicity and readOnly annotation make this a minor gap.
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 tool has zero parameters and the empty schema fully describes the input surface, so there is nothing for the description to add about parameter semantics. The baseline of 4 applies.
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: 'Verify the token and organization' and states what the tool reports (deployment capabilities, token owner, caller identity). This makes its role distinct from sibling data-operation tools like productive_get or productive_create.
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 clearly establishes the context — validating the Productive token and organization and surfacing what the deployment can do — so an agent knows to call it for connection/auth checks. It does not explicitly name alternatives or when-not conditions, but the absence of parameters and the verification purpose make the intended use obvious.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Execute a write that a prepare step staged. Pass the operation object back exactly as it was returned: it is hashed, and an altered operation is refused rather than run. Show the change to the person who asked for it before committing.
| Name | Required | Description | Default |
|---|---|---|---|
| operation | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the minimal readOnlyHint=false annotation, the description discloses two important behaviors: the operation is hashed and altered operations are refused, and the change must be shown to the requester before committing. This is valuable safety-relevant context for an agent.
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?
Three short, purposeful sentences with no fluff. Purpose, usage constraint, and mandatory approval step are all front-loaded and directly actionable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers what matters most for a commit operation: exact object reuse, hash enforcement, and user approval. It does not describe the success response or explicitly name a prepare tool, but the workflow is sufficiently constrained for correct invocation.
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, and the single nested 'operation' parameter has no field-level documentation. However, the description compensates by instructing the agent to pass the exact object back and explaining the hash invariant, which materially reduces the risk of misuse despite lacking field-by-field 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?
Description states a specific verb and resource: 'Execute a write that a prepare step staged.' This clearly identifies the tool as the commit step for a staged operation, distinguishing it from direct create/update/delete siblings by the staged-operation framing.
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 clear usage context: use after a prepare step, pass the operation object back exactly as returned, and show the change before committing. It does not explicitly name alternatives or say when not to use it, but the staged-operation workflow is unmistakable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create a record on any writable resource. Required attributes are checked first, and so is every attribute name. On a financial or administrative resource this returns a staged operation to review rather than creating anything — pass it to productive_commit_operation to go through with it.
| Name | Required | Description | Default |
|---|---|---|---|
| resource | Yes | ||
| attributes | No | Attributes to write, by their Productive names. Validated against the resource contract first, because Productive answers 200 and ignores an unknown attribute rather than reporting it. Custom-field values go in a nested `custom_fields` object keyed by field id. | |
| relationships | No | JSON:API relationships, where the resource accepts them. Most links are set through an `_id` attribute instead. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations only mark readOnlyHint=false, so the description carries the burden of explaining mutation nuance. It discloses the staging behavior, the validation-before-write behavior, and the fact that commit is required on certain resources. This is meaningful behavioral context beyond the schema.
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?
Three sentences, no filler, with the core action first, then validation, then the exception case. Every sentence adds 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?
For a create tool with no output schema, it explains the essential invocation path, including when commit is required. It could say more about return shape or failure behavior, but the description and schema together cover the necessary details.
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 already describes attributes and relationships in detail, covering 2 of 3 parameters. The tool-level description adds no parameter-specific detail; `resource` remains minimally described, though it is self-evident and explained at a high level as 'any writable resource.'
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 exactly what it does: create a record on any writable resource. The explicit mention of productive_commit_operation distinguishes it from update/delete/list/get siblings. No ambiguity about the operation performed.
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?
Explains the resource-type condition that changes behavior: financial/administrative resources stage an operation rather than creating, and directs the agent to productive_commit_operation to finalize. It doesn't enumerate exclusions versus update/delete, but for a create tool this is sufficient guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Delete a record. Needs its own switch beyond ordinary writes, and always returns a staged operation first. Many Productive types are meant to be archived rather than deleted — check productive_describe_resource for an archive action before reaching for this.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| resource | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare destructiveHint=true and readOnlyHint=false, so the description need not restate that. It adds non-obvious behavioral facts: deletion requires a separate switch and always returns a staged operation first. This goes beyond annotations and meaningfully sets 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 three concise sentences with the core purpose first, followed by two high-value caveats. Every sentence earns its place; there is no filler or repetition of schema/annotations.
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 destructive tool with two simple parameters and no output schema, the description provides essential behavior: staged operation first, archive exception, and switch requirement. The main gap is parameter semantics, but the operational guidance is strong enough to make the tool safely invocable.
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 define the `resource` or `id` parameters. 'Delete a record' hints that resource is the record type and id is its identifier, but this is too vague to clarify valid resource types or how the id is used. The description adds minimal value beyond the parameter names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action ('Delete') and resource ('a record'), making the tool's purpose clear. It also distinguishes the tool from create/update operations and explicitly references the archive alternative, differentiating it from a sibling behavior.
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 instructs the agent to check productive_describe_resource for an `archive` action before using delete, giving clear when-to-use-vs-alternative guidance. It also notes the requirement for a special switch beyond ordinary writes, which is a practical precondition for invocation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List the organization's custom fields with the id each value is written under, the object it applies to, and its options. Custom-field values are keyed by numeric id, not by display name — a name used as a key is accepted with 200 and stored nowhere.
| Name | Required | Description | Default |
|---|---|---|---|
| appliesTo | No | Narrow to fields on one object, matched against Productive's own wording for it. That wording is not this server's resource key — fields on people report "employees" — so call this without an argument first and pick from appliesToValues. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint annotation, the description discloses a critical behavioral quirk: custom-field values are keyed by numeric id, not display name, and using a name as a key is silently accepted with 200 but stored nowhere. This is exactly the kind of non-obvious behavior that prevents an agent from making a subtle mistake.
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 with no filler. It front-loads the purpose and return fields, then adds the essential behavior warning. Every sentence earns its place.
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 listing tool with one optional parameter, the description is complete: it names the resource, lists the output components, and flags the keying gotcha. The absence of an output schema is mitigated by the explicit statement of what is returned, and the readOnlyHint annotation covers safety 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?
The input schema has 100% description coverage and already explains appliesTo in detail, including the 'employees' example and the instruction to call without an argument first. The tool description itself adds no additional parameter semantics beyond the schema, so the baseline of 3 is appropriate.
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 uses a specific verb ('List'), names the resource ('organization's custom fields'), and specifies what is returned: id, object, and options. It clearly distinguishes this from siblings like productive_describe_resource, which would describe a resource's schema rather than enumerate custom fields.
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 its usage context clearly: call it to list custom fields and understand how values are keyed. The input schema adds explicit guidance to call without an argument first and then pick from appliesToValues, which is practical usage direction. It does not explicitly name alternative tools or state when not to use it, 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.
The exact contract for one resource, derived from Productive's own OpenAPI document: every filter field, the operators any field accepts, sort keys, report group keys, includable relationships, the writable attributes for create and update with the required ones marked, and any named actions. Read this before writing — it is the difference between a write that lands and a 200 that changed nothing.
| Name | Required | Description | Default |
|---|---|---|---|
| resource | Yes | Resource key, e.g. "tasks", "time_entries", "reports/time_reports". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds valuable behavioral context: output is derived from Productive's own OpenAPI document and includes specific contract elements, plus a warning about write no-ops that goes beyond the annotation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is dense but efficient: one sentence enumerates the full contract contents, and a second short sentence delivers a high-impact usage warning. Every phrase contributes meaning, with no filler or repetition of the schema.
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 introspection tool, the description fully specifies what output the agent should expect and when to call it. The lack of an output schema is compensated by the detailed enumeration of returned contract components.
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%, and the schema already documents the single 'resource' parameter with examples. The description adds no additional syntax, validation, or format details for the parameter, so it stays at the baseline where the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a precise verb-object relationship: it provides 'the exact contract for one resource' and enumerates the concrete contents such as filter fields, operators, sort keys, and writable attributes. This clearly distinguishes it from siblings like productive_get, productive_list, and productive_describe_custom_fields.
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 the agent to 'Read this before writing' and contrasts a successful write with 'a 200 that changed nothing,' giving a clear trigger for use. It does not explicitly name alternative tools or when not to use it, but the intended context is unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Read one record by id, optionally pulling related records in the same call rather than following relationship stubs one at a time.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| raw | No | ||
| include | No | ||
| resource | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already establishes safety, and the description adds meaningful behavior: optional eager-loading of related records in the same call. It does not explain raw mode or default include behavior, but the core read semantics are transparent and align 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?
The description is a single, well-structured sentence that front-loads the core purpose and then adds the key optional behavior. There is no redundant or filler language.
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 descriptions, the description is too sparse to fully support correct invocation. The agent still has to guess what 'resource' values are valid, what 'raw' does, and what the response shape looks like. The relationship-include guidance is helpful but not enough.
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%, so the description must compensate for all four parameters. It explains the purpose of 'id' and loosely hints at 'include', but it never clarifies 'resource' or 'raw', which are both undocumented in the schema and the description. 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 uses a specific verb ('Read') and clear object ('one record by id'), which distinguishes it from sibling tools like productive_list and productive_create. The added phrase about pulling related records also separates it from naive relationship-stub traversal.
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 conveys when to use the tool: when you need a single record by id and optionally want related records in one call rather than following stubs. It does not explicitly exclude alternatives like productive_list for multiple records, so it stops short of full when-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List any Productive resource with server-side filtering, sorting, includes and paging. Also how the 26 report endpoints are read: use resource "reports/time_reports" (and the rest) with group to get aggregated rows instead of records. Results are flattened out of JSON:API — attributes hoisted, included records inlined into their relationship — with total and nextPage so a page is never mistaken for the whole answer.
| Name | Required | Description | Default |
|---|---|---|---|
| raw | No | Return Productive's JSON:API envelope untouched instead of flat records. | |
| page | No | ||
| sort | No | Sort keys; prefix with "-" for descending, e.g. ["-created_at"]. | |
| group | No | Report aggregation key. Only the reports/* resources accept one. | |
| filters | No | Server-side filters. A bare value means equality: {"assignee_id":"5"}. An array is comma-joined: {"id":["1","2"]}. An operator object narrows further: {"created_at":{"gt":"2026-01-01"}} — the ONLY operators Productive accepts are contains, eq, gt, lt, not_contain, not_eq. There is no gte/lte, so for an inclusive date range use the resource's own after/before filter fields instead. Logical groups nest: {"$op":"and","0":{"assignee_id":{"eq":"5"}},"1":{"company_id":{"eq":"7"}}}. Field names are validated against the resource — call productive_describe_resource for the list. | |
| include | No | Relationships to pull in the same call, e.g. ["assignee","project"]. | |
| resource | Yes | One of 132 resource keys; see productive_search_capabilities. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds significant behavioral detail beyond the readOnlyHint annotation: results are flattened out of JSON:API, attributes are hoisted, included records are inlined, and responses include total and nextPage so the agent understands pagination is partial. It also clarifies that report resources return aggregated rows rather than records. This goes well beyond the minimal safety signal.
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 at three sentences and front-loads the core purpose. Each sentence provides meaningful information, though the phrase 'Also how the 26 report endpoints are read' is slightly awkward and could be streamlined. Overall it is efficient without unnecessary detail.
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 explains the return shape well: flattened records, inlined relationships, total, and nextPage. It also covers the special report behavior and aggregated rows. For a complex 7-parameter listing tool with 132 possible resources, this is sufficiently complete to guide correct invocation.
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 86%, so the input schema already documents most parameters well. The description adds a little context around group for report endpoints, but it mostly summarizes capabilities like filtering and paging rather than meaningfully extending the schema's parameter documentation. Baseline 3 is appropriate because the schema carries the parameter-semantic burden.
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 any Productive resource with filtering, sorting, includes, and paging. It also explicitly handles the special behavior of the 26 report endpoints, distinguishing this from siblings like productive_get and productive_search_capabilities. The verb 'List' and resource scope are 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 guidance on when to use this tool, including the specific instruction to use resource 'reports/time_reports' with group for aggregated report rows. It provides the main usage context but does not explicitly enumerate exclusions or alternatives such as 'use productive_get for a single record.' The context is clear enough that an agent can choose correctly in most cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run one of Productive's named verbs: archive, restore, close, open, approve, reject, copy, finalize, send, reposition and the rest. Actions named bulk_* act on every record the filter matches rather than one, and refuse to run without an explicit filter. productive_describe_resource lists the actions a resource has.
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | Required for actions that act on one record. | |
| action | Yes | Action name, e.g. "archive", "approve", "send". | |
| filters | No | Server-side filters. A bare value means equality: {"assignee_id":"5"}. An array is comma-joined: {"id":["1","2"]}. An operator object narrows further: {"created_at":{"gt":"2026-01-01"}} — the ONLY operators Productive accepts are contains, eq, gt, lt, not_contain, not_eq. There is no gte/lte, so for an inclusive date range use the resource's own after/before filter fields instead. Logical groups nest: {"$op":"and","0":{"assignee_id":{"eq":"5"}},"1":{"company_id":{"eq":"7"}}}. Field names are validated against the resource — call productive_describe_resource for the list. | |
| resource | Yes | ||
| attributes | No | Attributes to write, by their Productive names. Validated against the resource contract first, because Productive answers 200 and ignores an unknown attribute rather than reporting it. Custom-field values go in a nested `custom_fields` object keyed by field id. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations only provide readOnlyHint=false, so the description carries the behavioral burden. It discloses two non-obvious behaviors: bulk_* actions operate on every record the filter matches, and they refuse to run without an explicit filter. The schema also surfaces that Productive answers 200 and silently ignores unknown attributes, adding important behavioral context beyond the annotation.
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 core purpose, then immediately covers the most dangerous edge cases (bulk scope and filter requirement), and ends with a useful discovery pointer. Every sentence earns its place.
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 complex 5-parameter tool with nested filter objects and no output schema, the description and schema together cover the essential invocation details: valid action classes, bulk semantics, filter requirements, field validation, and discovery via productive_describe_resource. The only notable gap is the lack of any mention of what the response looks like or error behavior beyond the silent-attribute-ignore note, which is a minor omission for an action-triggering 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?
Schema description coverage is roughly 80%, and the schema already provides rich semantics for filters, attributes, and id. The description adds the bulk-vs-single distinction and the explicit-filter requirement, which complements the filters parameter. Given this high schema coverage, the baseline of 3 is appropriate; the description does not need to re-document each 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 names a specific verb ('Run') and a clear resource class ('one of Productive's named verbs'), then lists concrete examples: archive, restore, close, open, approve, reject, copy, finalize, send, reposition. It also distinguishes single-record actions from bulk_* actions, so the tool's purpose is unmistakable and well differentiated from the sibling CRUD 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 clearly states that bulk_* actions require an explicit filter and calls out productive_describe_resource as the way to discover which actions a resource supports. It does not, however, give explicit when-not-to-use guidance against siblings like productive_update or productive_delete, 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.
Find the right resource, tool or guide for a subject. Start here: Productive names its resources its own way (a budget is a deal, a person is a person, a board column is a workflow_status), and guessing wastes calls. Returns matching tools, workflow guides, and resources with the operations each supports.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | Subject to search for, e.g. "invoice", "capacity", "custom field", "time". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With readOnlyHint=true already in annotations, the description adds useful behavioral context: it returns matching tools, workflow guides, and resources along with the operations they support. It also discloses the important quirk that resource names may not match user expectations. No contradiction 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?
Three sentences, front-loaded with the core purpose, and every sentence adds value: what it finds, where to start, and what it returns. The naming examples are illustrative without being verbose.
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 search tool with only two parameters and no output schema, the description covers purpose, scoping context, and return categories well. It does not detail result shape or empty-result behavior, but those are not critical for selecting and invoking this 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 description reinforces that 'query' is a subject to search for, but the schema already covers that with examples. The 'limit' parameter is not described in the description, and with only 50% schema description coverage, the description does not fully compensate. The parameter is simple enough that the gap is moderate rather than severe.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Find') and resource ('resource, tool or guide for a subject') and clearly distinguishes this as the starting point for discovery. It explains the tool's role relative to siblings by highlighting Productive's naming conventions, so an agent knows this is a search/capability lookup, not a data operation.
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?
'Start here' explicitly tells the agent when to use this tool, and the mention of Productive's non-obvious naming ('a budget is a deal') explains why guessing would waste calls. It does not name alternative sibling tools or state when not to use it, but the contextual guidance is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Log a time entry without looking up what Productive insists on. Time is stored in MINUTES — pass hours and it converts. The service (the budget line the work bills to, not the task) is resolved from the task when not given, and the person is resolved from the authenticated caller, so time lands on the right person even though the server holds one shared token.
| Name | Required | Description | Default |
|---|---|---|---|
| date | Yes | Date of the work, YYYY-MM-DD. | |
| note | No | ||
| hours | No | Time worked in hours; converted to minutes. | |
| taskId | No | Task worked on. Also used to resolve the service. | |
| minutes | No | Time worked, in minutes. | |
| personId | No | Whose time this is. Defaults to the resolved caller. | |
| serviceId | No | Service (budget line) to bill to; overrides the task lookup. | |
| billableMinutes | No | Billable portion, if it differs from the time worked. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With only readOnlyHint=false in annotations, the description carries the full burden of behavioral disclosure. It clearly reveals that hours are converted to minutes, the service is derived from the task when absent, and the person is resolved from the authenticated caller despite a shared server token. This is specific and valuable transparency.
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?
Three sentences, each earning its place: the first states the action, the second explains the critical unit conversion, and the third clarifies lookup defaults and authentication behavior. It is dense without being bloated, and the essential action is front-loaded.
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 an 8-parameter tool with no output schema, the description covers the core call flow well: what to log, how units are handled, which IDs can be omitted, and how resolution works. It does not describe the return value or what happens when conflicting inputs like hours and minutes are both supplied, but these are minor gaps given how much is already explained.
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 88%, so the schema already documents most parameters. The description adds real value by explaining the hours-to-minutes conversion, the distinction between service and task, and the fallback resolution behavior for serviceId and personId. It does not explain every edge case, such as providing both hours and minutes, but it meaningfully supplements the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'Log a time entry.' It goes beyond the title by explaining the key behavior that makes this tool distinct from generic create/list tools: it avoids manual lookups and handles conversions, making the intent unmistakable.
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 practical context: service can be omitted and is resolved from the task, person can be omitted and is resolved from the caller. This tells an agent when it can avoid passing certain parameters, though it does not explicitly name alternatives or state when not to use the tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Update a record. PATCH semantics, verified live: attributes you omit keep their values, so there is no need to resend the whole record. On a financial or administrative resource this returns a staged operation instead of writing.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| resource | Yes | ||
| attributes | No | Attributes to write, by their Productive names. Validated against the resource contract first, because Productive answers 200 and ignores an unknown attribute rather than reporting it. Custom-field values go in a nested `custom_fields` object keyed by field id. | |
| relationships | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With only readOnlyHint=false provided by annotations, the description carries a meaningful behavioral burden and does well: it discloses PATCH semantics, confirms omitted fields are retained, and warns that certain resources return a staged operation instead of writing. It does not cover permissions or reversibility, but it adds real behavioral facts beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short and front-loaded with the key PATCH behavior. The first sentence duplicates the title, which costs a little, but every other sentence carries useful operational meaning without padding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers core PATCH semantics and the staged-operation caveat, but it does not explain what a staged operation should be followed by, especially given the sibling productive_commit_operation. There is also no output schema and the description does not say what a normal update returns, and relationships remain undocumented. This leaves the agent needing to infer important follow-up behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is low at 25%, and the description does add value for the attributes parameter by explaining that omitted attributes keep their values. However, it gives no semantic guidance for resource, id, or relationships, and the custom_fields nesting behavior lives only in the schema, not the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear action on a record and immediately clarifies PATCH semantics, which distinguishes it from read-only siblings like productive_get and productive_list. It is slightly repetitive with the title but unambiguous about the operation's purpose.
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 useful context: partial updates preserve omitted attributes, so no full resend is needed, and financial/admin resources return staged operations instead of writing. However, it does not explicitly say when to choose this over productive_create, productive_delete, or productive_run_action, so usage versus alternatives is mostly implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.2.0productive_check_connectionproductive_commit_operationproductive_createproductive_deleteproductive_describe_custom_fieldsproductive_describe_resourceproductive_getproductive_listproductive_run_actionproductive_search_capabilitiesproductive_track_timeproductive_updateScored across 12 tools
Each tool targets a distinct operation: reading one record, listing, creating, updating, deleting, running a named action, staging/committing writes, tracking time, or performing a meta/introspection task. Even the closer pairs like get/list and describe_resource/describe_custom_fields are clearly separated by resource scope and purpose.
All tools follow the snake_case productive_<verb> pattern, with most using verb_noun (productive_check_connection, productive_run_action) and a few using bare verbs (productive_get, productive_list). This is consistent and predictable, with no style mixing or vague duplicate verbs.
Twelve tools is well within the ideal scope for a broad API integration server. The set covers generic CRUD, named actions, time tracking, staged writes, and meta/discovery tools without ballooning into redundant or overly granular endpoints.
The server provides full lifecycle coverage: read, list, create, update, delete, run actions, and commit staged operations, plus support for custom fields and resource contracts. The meta tools fill the gaps that typically cause agent failures, such as knowing resource-specific filters, writable attributes, and custom-field keying.
Agent-complete, permission-scoped product operations for Priorify workspaces.
Supervised API-write gateway for AI agents with policy, human approval and execution receipts.
Permission-aware access to Wayfront clients, orders, projects, billing, tickets, and more.
Read time entries, projects, clients, tasks and invoices; log and update tracked time.