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Glama

push_context

Injects live news, competitor moves, or price changes into every persona card from the next simulation round, grounding AI buyer reactions in current reality.

Instructions

While a simulation is running (or before it starts), push what people are saying today into every persona card from the next round on: a news item, a competitor's move, a real thread you just read with your browser, a price change. Each update is one or two sentences; the runner re-reads the file every round. Use it to ground the rounds in live reality.

Input Schema

NameRequiredDescriptionDefault
textYes
roundNo
sourceNo
simulation_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It usefully discloses that updates flow into every persona card from the next round on, that each update is one or two sentences, and that the runner re-reads the file every round. However, it omits whether updates append or overwrite, permission/auth needs, error conditions, and what the tool returns.

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?

The main action and effect are front-loaded, and the examples earn their place by making the expected input concrete. It is slightly run-on and includes a somewhat promotional closing sentence, but overall it is appropriately sized.

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

Completeness3/5

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

For a four-parameter mutation tool with no annotations, no output schema, and zero schema description coverage, the description gives useful runtime context but leaves significant gaps. An agent understands the intent and timing, but not fully how to populate or interpret all parameters, nor what to expect operationally.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must explain all four parameters. It implicitly describes 'text' as a one- or two-sentence update and gestures at 'round' via 'from the next round on', but it never explains 'simulation_id' or 'source' and gives no format expectations for optional fields.

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?

The description states a specific action: pushing real-world context into persona cards from the next simulation round onward. It clearly distinguishes the tool's role from broad simulation siblings like start_live or results, though it does not explicitly name alternatives.

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?

It gives clear timing guidance ('while a simulation is running (or before it starts)') and a concrete purpose ('ground the rounds in live reality'), with examples of acceptable inputs. It does not explicitly state when not to use it or name alternative tools.

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