Call For Me
Server Details
Give your AI agent a phone. Place outbound calls to US businesses to ask, book, or confirm.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.7/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: one places a call, the other polls its status. There is no overlap or ambiguity.
Both tool names follow a consistent verb_noun pattern in snake_case: get_call_status and place_call.
With only 2 tools, the coverage is minimal but sufficient for the core workflow of placing and monitoring a call. An additional cancel tool could be useful, but the count is appropriate for a focused utility.
The tools cover the essential actions: initiating a call and retrieving results. Missing a cancel/retry capability, but the core lifecycle is well-supported.
Available Tools
2 toolsget_call_statusAInspect
Poll the status of a call previously placed with place_call. Returns a CallOutcome with one of these statuses (in roughly likely order): queued/placing/ringing/connected — call still in progress, poll again in a few seconds completed — call finished cleanly, structured result available partial — call finished but some questions unanswered voicemail — recipient was voicemail; identified message left no_answer — recipient never picked up busy — recipient line was busy invalid_number — number is not in service rejected_ai — recipient declined to talk to A.I. (do not retry) needs_user_input — recipient needs info only the user can provide online_only — recipient said the task can only be done online timed_out — call exceeded max duration carrier_failure — telephony provider error (safe to retry later) api_error — internal failure (safe to retry)
Always carries a free-form exit_reason string suitable for showing the user.
| Name | Required | Description | Default |
|---|---|---|---|
| call_id | Yes | The call_id returned by place_call. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description fully discloses behavior: returns statuses with explanations, exit_reason string, and retry guidance. It clearly communicates that it's a polling (read) operation.
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 structured with a list of statuses, which is helpful. It front-loads the purpose. Slightly verbose but each element serves a purpose.
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 no output schema and no annotations, the description is remarkably complete: it lists all statuses, their meanings, and retry guidance. It also mentions the exit_reason field.
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% and the description adds no new information beyond the schema's description of call_id. Baseline 3 is appropriate as it does not enhance 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?
The description clearly states the tool polls status of a call placed with place_call, distinguishing it from its sibling. The verb 'poll' and resource 'call status' are explicit.
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 explains when to use (after place_call), when to poll again (in-progress statuses), and when not to retry (rejected_ai) or safe to retry (carrier_failure, api_error). This provides clear usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
place_callAInspect
Place an outbound phone call to a US business or person. The A.I. assistant on the call will identify itself as A.I., follow your instructions, and return a structured outcome via get_call_status.
USE THIS TOOL WHEN: the user wants to make a phone call, contact a business by phone, ask about hours/pricing/availability, book a reservation, gather info that's not online, follow up on a service request, or do any task that requires talking to a human on the phone.
LIMITATIONS:
US phone numbers only (E.164 starting with +1). International is not yet supported.
English language only.
Cannot call emergency services (911, 988, etc.) — these will be rejected.
The A.I. will NOT commit to bookings/payments without user confirmation unless safety.must_confirm_with_user is explicitly set to false.
Default 5-minute call duration cap.
Returns immediately with a call_id. Poll get_call_status(call_id) for the result.
| Name | Required | Description | Default |
|---|---|---|---|
| user | Yes | ||
| safety | No | ||
| target | Yes | ||
| instructions | Yes | The conversation goals/script the AI should follow. Agent owns this. We wrap it with mandatory safety layer (disclosure, hangup, voicemail, graceful-exit rubric). Be specific — what to ask, what tone, what to do if recipient declines. | |
| output_schema | No | JSON Schema for the structured outcome you want extracted from the transcript. | |
| idempotency_key | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses behavior: AI identifies itself, follows instructions, returns call_id immediately, default 5-min cap, no emergency services, and requires user confirmation unless overridden. No contradictions.
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 well-structured with sections, front-loading the core action and usage guidance. While slightly verbose, each sentence adds value and there is no repetition of schema details. It earns its length.
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 tool with no output schema, the description covers usage, limitations, return behavior, and interaction with get_call_status. It lacks detailed parameter semantics for some fields but is sufficient for an AI agent to select and use 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?
Schema coverage is low (33%) but the description adds context for key parameters: instructions are explained as conversation goals, idempotency_key is implied by immediate return, and output_schema is mentioned for structured extraction. The 'safety' and 'user' sub-objects are mostly left to schema but the description's overall context helps.
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 places outbound phone calls to US businesses or persons, distinguishing it from the sibling get_call_status which retrieves results. The verb 'place' and resource 'call' are specific and 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 explicitly lists when to use the tool ('USE THIS TOOL WHEN:') with concrete scenarios like contacting businesses or gathering info not online. It also notes alternatives (poll with get_call_status) and includes limitations (US only, English, no emergency).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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