Returns this connector's instructions — what noticed is and how to use it. Replays the server instructions text on demand; call anytime to re-read.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| instructions | Yes |
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": false,
+ "properties": {
+ "instructions": {
+ "type": "string"
+ }
+ },
+ "required": [
+ "instructions"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is fully covered. The description adds the useful context that the tool 'replays the server instructions text on demand,' but does not elaborate on response format or potential caching 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?
Two concise sentences with no filler. The core action is front-loaded, and the secondary note about when to call is brief and useful.
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 a rich set of annotations and an output schema, the description fully covers what the tool does, what it returns, and when to use it. Nothing essential 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 tool has zero parameters, so there is no parameter semantics burden. Schema description coverage is 100% and no parameters exist, making the description sufficient without adding parameter detail.
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 ('Returns') and resource ('this connector's instructions'), and clarifies what the instructions contain ('what noticed is and how to use it'). It is unambiguous and does not overlap with any sibling tool.
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 directly says 'call anytime to re-read,' which gives clear when-to-use guidance. It does not explicitly name alternatives or exclusions, but none are needed given the tool's self-referential purpose.
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.