Your own developer profile as noticed sees it — GitHub + LinkedIn + derived tags. Grounds 'about me' answers.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| ok | Yes | Whether noticed completed the operation. | |
| data | No | The operation result when ok is true. | |
| error | No | A human-readable error when ok is false. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / (root)Previous value: -nullNew value: +{
+ "$schema": "http://json-schema.org/draft-07/schema#",
+ "additionalProperties": true,
+ "properties": {
+ "data": {
+ "additionalProperties": true,
+ "description": "The operation result when ok is true.",
+ "properties": {},
+ "type": "object"
+ },
+ "error": {
+ "description": "A human-readable error when ok is false.",
+ "type": "string"
+ },
+ "ok": {
+ "description": "Whether noticed completed the operation.",
+ "type": "boolean"
+ }
+ },
+ "required": [
+ "ok"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish the tool as read-only, idempotent, and non-destructive. The description adds meaningful context about what the profile contains and how it is framed, which goes beyond safety traits, though it could clarify the odd 'as noticed sees it' phrasing.
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 short sentences with no filler: it states the resource, its content, and its intended use. Information is front-loaded in the phrase 'Your own developer profile'.
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 zero-parameter read-only tool with an output schema, the description is largely complete: it names the resource, content, and intended use case. The phrase 'as noticed sees it' is ambiguous and could be clearer, but it does not prevent an agent from invoking 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 tool has zero parameters, so the description does not need to explain parameters. With no input schema properties, the baseline for this dimension applies and no parameter meaning is missing.
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 indicates this tool returns the calling developer's own profile, with GitHub, LinkedIn, and derived tags as content. It helps distinguish my_profile from profile-related siblings by emphasizing 'your own', though it omits an explicit verb like 'retrieve' or 'get'.
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 "Grounds 'about me' answers" explicitly identifies a use case: answering questions about the caller. It gives clear context but does not mention exclusions or alternatives among sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The tool set is organized around distinct resources, and the descriptions work hard to separate them, but several close pairs remain easy to confuse: add_memory vs memory_save vs add_note, accept_identity_match vs suggest_identity_match, and dismiss_identity_match vs mark_different_people. An agent will often need to read very subtle signals (who originated the content, pending vs initiating a merge, soft vs durable rejection) to pick the right tool.
Most tools follow a clear verb_noun snake_case pattern like create_list, update_person, and delete_view, which is readable and mostly predictable. However, the memory tools break the pattern (memory_save, memory_get, memory_search instead of save_memory/get_memory/search_memory), and a few noun-style names (my_profile, network_summary, account_status) add inconsistency.
At 57 tools, this is an extremely large surface that exceeds the calibration threshold for an extreme mismatch. The scope is broad, but many tools are micro-specialized variations of the same concept, such as four memory-related tools and seven identity-match tools, which makes the count feel inflated rather than well-scoped.
The tool set provides thorough lifecycle coverage for the core domain: people can be added, updated, searched, and removed; lists, views, actions, and scheduled tasks have create/read/update/delete; and identity matching has accept, dismiss, differentiate, and suggest paths. Minor gaps exist, such as no direct memory/note deletion or intro deletion, but agents can generally complete workflows without hitting dead ends.